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Lapola DM, Pinho P, Barlow J, Aragão LEOC, Berenguer E, Carmenta R, Liddy HM, Seixas H, Silva CVJ, Silva-Junior CHL, Alencar AAC, Anderson LO, Armenteras D, Brovkin V, Calders K, Chambers J, Chini L, Costa MH, Faria BL, Fearnside PM, Ferreira J, Gatti L, Gutierrez-Velez VH, Han Z, Hibbard K, Koven C, Lawrence P, Pongratz J, Portela BTT, Rounsevell M, Ruane AC, Schaldach R, da Silva SS, von Randow C, Walker WS. The drivers and impacts of Amazon forest degradation. Science 2023; 379:eabp8622. [PMID: 36701452 DOI: 10.1126/science.abp8622] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Approximately 2.5 × 106 square kilometers of the Amazon forest are currently degraded by fire, edge effects, timber extraction, and/or extreme drought, representing 38% of all remaining forests in the region. Carbon emissions from this degradation total up to 0.2 petagrams of carbon per year (Pg C year-1), which is equivalent to, if not greater than, the emissions from Amazon deforestation (0.06 to 0.21 Pg C year-1). Amazon forest degradation can reduce dry-season evapotranspiration by up to 34% and cause as much biodiversity loss as deforestation in human-modified landscapes, generating uneven socioeconomic burdens, mainly to forest dwellers. Projections indicate that degradation will remain a dominant source of carbon emissions independent of deforestation rates. Policies to tackle degradation should be integrated with efforts to curb deforestation and complemented with innovative measures addressing the disturbances that degrade the Amazon forest.
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Affiliation(s)
- David M Lapola
- Laboratório de Ciência do Sistema Terrestre - LabTerra, Centro de Pesquisas Meteorológicas e Climáticas Aplicadas à Agricultura - CEPAGRI, Universidade Estadual de Campinas, Campinas, SP, Brazil
| | - Patricia Pinho
- Instituto de Pesquisas Ambientais da Amazônia, Brasília, DF, Brazil
| | - Jos Barlow
- Lancaster Environment Centre, Lancaster University, Lancaster, UK
| | - Luiz E O C Aragão
- Instituto Nacional de Pesquisas Espaciais, São José dos Campos, SP, Brazil.,Geography, University of Exeter, Exeter, UK
| | - Erika Berenguer
- Lancaster Environment Centre, Lancaster University, Lancaster, UK.,Environmental Change Institute, University of Oxford, Oxford, UK
| | | | - Hannah M Liddy
- Columbia Climate School, Columbia University, New York, NY, USA.,NASA Goddard Institute for Space Studies, New York, NY, USA
| | - Hugo Seixas
- Laboratório de Ciência do Sistema Terrestre - LabTerra, Centro de Pesquisas Meteorológicas e Climáticas Aplicadas à Agricultura - CEPAGRI, Universidade Estadual de Campinas, Campinas, SP, Brazil
| | - Camila V J Silva
- Instituto de Pesquisas Ambientais da Amazônia, Brasília, DF, Brazil.,Lancaster Environment Centre, Lancaster University, Lancaster, UK.,BeZero Carbon Ltd, London, UK
| | - Celso H L Silva-Junior
- Institute of Environment and Sustainability, University of California, Los Angeles, CA, USA.,Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA.,Programa de Pós-graduação em Biodiversidade e Conservação, Universidade Federal do Maranhão - UFMA, São Luís, MA, Brazil
| | - Ane A C Alencar
- Instituto de Pesquisas Ambientais da Amazônia, Brasília, DF, Brazil
| | - Liana O Anderson
- Centro Nacional de Monitoramento e Alertas de Desastres Naturais, São José dos Campos, SP, Brazil
| | | | | | - Kim Calders
- Computational & Applied Vegetation Ecology Laboratory, Department of Environment, Ghent University, Belgium.,School of Forest Sciences, University of Eastern Finland, Joensuu, Finland
| | | | | | | | - Bruno L Faria
- Instituto Federal de Educação, Ciência e Tecnologia do Norte de Minas Gerais, Diamantina, MG, Brazil
| | | | - Joice Ferreira
- Empresa Brasileira de Pesquisa Agropecuária, Belém, PA, Brazil
| | - Luciana Gatti
- Instituto Nacional de Pesquisas Espaciais, São José dos Campos, SP, Brazil
| | | | | | - Kathleen Hibbard
- National Aeronautics and Space Administration Headquarters, Washington, DC, USA
| | - Charles Koven
- Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Peter Lawrence
- National Center for Atmospheric Research, Boulder, CO, USA
| | - Julia Pongratz
- Max Planck Institute for Meteorology, Hamburg, Germany.,Ludwig-Maximilians University of Munich, Munich, Germany
| | | | - Mark Rounsevell
- Karlsruhe Institute of Technology, Karlsruhe, Germany.,University of Edinburgh, Edinburgh, UK
| | - Alex C Ruane
- NASA Goddard Institute for Space Studies, New York, NY, USA
| | | | | | - Celso von Randow
- Instituto Nacional de Pesquisas Espaciais, São José dos Campos, SP, Brazil
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Impact of Land Use Change on Tree Diversity and Aboveground Carbon Storage in the Mayombe Tropical Forest of the Democratic Republic of Congo. LAND 2022. [DOI: 10.3390/land11060787] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
The Mayombe tropical forest has experienced dramatic changes over several decades due to human activities. However, the impact of these changes on tree biodiversity and ecosystem services has not been studied yet. Such a study could advance the current knowledge on tree biodiversity and carbon storage within the Mayombe forest, which is presently under high anthropogenic pressures. This information could benefit decision-makers to design and implement strategies for biodiversity conservation and sustainable natural resource utilization. As such, biodiversity surveys were conducted within the forest under different land utilization regimes. To evaluate the effect of human utilization on tree biodiversity and ecosystem services (carbon storage), land was classified into three categories based on the intensity of human utilization: low utilization, moderate utilization, and high utilization. Additionally, the study evaluated the recovery potential of the disturbed forest under both moderate and high utilization, after abandonment for 10 and 20 years. Tree diameter and height were measured for all trees whose diameter at breast height was greater than or equal to 10 cm. Our findings revealed that forest land with both high and moderate utilization regimes, and having no regulation, resulted in the decline of tree species richness, tree species diversity, and carbon storage. The magnitude of decrease was greater in high utilization compared to moderate utilization regimes. On the other hand, high values of biodiversity indices and carbon storage were observed in the low utilization regime. This study also demonstrated that fallow land that had been left undisturbed for more than 10 years, but had experienced both high and moderate utilization regimes, could reasonably recover carbon storage, and an acceptable level of tree species biodiversity can be achieved. However, there remains a significant difference when compared with the original level in the low utilization regime, suggesting that the Mayombe forest takes longer to recover. Based on the findings on tree biodiversity and carbon storage over the recovery trajectory, this study improves the understanding of the degraded forest restoration process within the Mayombe forest. It is therefore necessary to formulate new strategies to regulate forest land utilization within the Mayombe forest. This will ensure sustainability and availability of all ecosystem services this forest provides to a human population that strongly depends on it for their survival.
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Assessment and Prediction of Carbon Storage Based on Land Use/Land Cover Dynamics in the Tropics: A Case Study of Hainan Island, China. LAND 2022. [DOI: 10.3390/land11020244] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Land use and land cover (LULC) change in tropical regions can cause huge amounts of carbon loss and storage, thus significantly affecting the global climate. Due to the differences in natural and social conditions between regions, it is necessary to explore the correlation mechanism between LULC and carbon storage changes in tropical regions from a broader geographical perspective. This paper takes Hainan Island as the research object, through the integration of the CA-Markov and Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) models, based on multi-source data, analyses the dynamics of LULC and carbon storage from 1992 to 2019 and the relationship between the two, and predicts future LULC and carbon storage under different scenarios. The results show that (1) the built-up land area of Hainan Island expanded from 103.59 km2 to 574.83 km2 from 1992 to 2019, an increase of 454.91%; the area of cropland and shrubland decreased; and the area of forest increased. (2) Carbon storage showed an upward trend during 1992–2000, and a downward trend during 2000–2019. Overall, LULC changes during 1992–2019 reduced carbon storage by about 1.50 Tg. (3) The encroachment of cropland in built-up land areas is the main reason for the reduction of carbon storage. The conversion of shrubland to forest is the main driving force for increasing carbon storage. The increase and decrease of carbon storage have obvious spatial clustering characteristics. (4) In the simulation prediction, the natural trend scenario (NT), built-up land priority scenario (BP) and ecological priority scenario (EP) reduce the carbon storage of Hainan Island, and the rate of decrease is BP> NT > EP. The cropland priority scenario (CP) can increase the LULC carbon storage, and the maximum increase in 2050 can reach 0.79 Tg. This paper supplements and improves the understanding of the correlation between LULC and carbon storage changes in tropical regions, and can provide guidance for the optimization of LULC structure in tropical regions with high economic development from a low-carbon perspective.
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Fine-Scale Improved Carbon Bookkeeping Model Using Landsat Time Series for Subtropical Forest, Southern China. REMOTE SENSING 2022. [DOI: 10.3390/rs14030753] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Subtropical forests easily suffer anthropogenic disturbance, including deforestation and reforestation management, which both highly affect the carbon pools. This study proposes spatial-temporal tracking of the carbon density dynamics to improve bookkeeping in the carbon model and applied to subtropical forest activities in Guangzhou, southern China, during the period of 1995 to 2014. Based on the overall accuracy of 87.5% ± 1.7% for forest change products using Landsat time series (LTS), we found that this is a typical period of deforestation conversion to reforestation activity accompanied with urbanization. Additionally, linear regression, random forest regression and allometric growth fitting were proposed by using forest field plots to obtain reliable per-pixel carbon density estimations. The cross-validation (CV) of random forest with LTS-derived parameters reached the highest accuracy of R2 and RMSE of 0.763 and 7.499 Mg ha−1. The RMES of the density estimation ranged between 78 and 84% of the mean observed biomass in the study area, which outperformed previous studies. Over the 20-year period, the study results showed that the explicit carbon emissions were (6.82 ± 0.26) × 104 Mg C yr−1 from deforestation; emissions increased to (1.02 ± 0.04) × 105 Mg C yr−1 given the implicit carbon not yet released to the atmosphere in the form of decomposing slash and wood products. In addition, a carbon uptake of about 1.91 ± 0.73 × 105 Mg C yr−1, presented as the net carbon pool. Based on the continuous detection capability, biennial reforestation activity has increased carbon density by a growth rate of 1.55 Mg ha−1, and the emission factors can be identified with LTS-derived parameters. In general, the study realizes the spatiotemporal improvement of carbon density and flux dynamics tracking, including the abrupt and graduate change based on fine-scale forest activity. It can provide more comprehensive and detailed feedback on the carbon source and sink change process of forest activities and disturbances.
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Piffer PR, Calaboni A, Rosa MR, Schwartz NB, Tambosi LR, Uriarte M. Ephemeral forest regeneration limits carbon sequestration potential in the Brazilian Atlantic Forest. GLOBAL CHANGE BIOLOGY 2022; 28:630-643. [PMID: 34665911 DOI: 10.1111/gcb.15944] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2021] [Accepted: 10/07/2021] [Indexed: 06/13/2023]
Abstract
Although deforestation remains widespread in the tropics, many places are now experiencing significant forest recovery (i.e., forest transition), offering an optimistic outlook for natural ecosystem recovery and carbon sequestration. Naturally regenerated forests, however, may not persist, so a more nuanced understanding of the drivers of forest change in the tropics is critical to ensure the success of reforestation efforts and carbon sequestration targets. Here we use 35 years of detailed land cover data to investigate forest trajectories in 3014 municipalities in the Brazilian Atlantic Forest (AF), a biodiversity and conservation hotspot. Although deforestation was evident in some regions, deforestation reversals, the typical forest transition trajectory, were the prevalent trend in the AF, accounting for 38% of municipalities. However, simultaneous reforestation reversals in the region (13% of municipalities) suggest that these short-term increases in native forest cover do not necessarily translate into persistent trends. In the absence of reversals in reforestation, forests in the region could have sequestered 1.75 Pg C, over three times the actual estimated carbon sequestration (0.52 Pg C). We also showed that failure to distinguish native and planted forests would have masked native forest cover loss in the region and overestimated reforestation by 3.2 Mha and carbon sequestration from natural forest regeneration by 0.37 Pg C. Deforestation reversals were prevalent in urbanized municipalities with limited forest cover and high agricultural productivity, highlighting the importance of favorable socioeconomic conditions in promoting reforestation. Successful forest restoration efforts will require development and enforcement of environmental policies that promote forest regeneration and ensure the permanence of regrowing forests. This is crucial not only for the fate and conservation of the AF, but also for other tropical nations to achieve their restoration and carbon sequestration commitments.
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Affiliation(s)
- Pedro R Piffer
- Department of Ecology, Evolution, and Environmental Biology, Columbia University, New York, New York, USA
| | - Adriane Calaboni
- Center of Engineering, Modeling and Applied Social Sciences, Federal University of ABC, Santo André, SP, Brazil
| | - Marcos R Rosa
- Department of Geography, University of São Paulo, São Paulo, SP, Brazil
| | - Naomi B Schwartz
- Department of Geography, University of British Columbia, Vancouver, British Columbia, Canada
| | - Leandro R Tambosi
- Center of Engineering, Modeling and Applied Social Sciences, Federal University of ABC, Santo André, SP, Brazil
- Department of Ecology, University of São Paulo, São Paulo, SP, Brazil
| | - María Uriarte
- Department of Ecology, Evolution, and Environmental Biology, Columbia University, New York, New York, USA
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Change Detection of Selective Logging in the Brazilian Amazon Using X-Band SAR Data and Pre-Trained Convolutional Neural Networks. REMOTE SENSING 2021. [DOI: 10.3390/rs13234944] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
It is estimated that, in the Brazilian Amazon, forest degradation contributes three times more than deforestation for the loss of gross above-ground biomass. Degradation, in particular those caused by selective logging, result in features whose detection is a challenge to remote sensing, due to its size, space configuration, and geographical distribution. From the available remote sensing technologies, SAR data allow monitoring even during adverse atmospheric conditions. The aim of this study was to test different pre-trained models of Convolutional Neural Networks (CNNs) for change detection associated with forest degradation in bitemporal products obtained from a pair of SAR COSMO-SkyMed images acquired before and after logging in the Jamari National Forest. This area contains areas of legal and illegal logging, and to test the influence of the speckle effect on the result of this classification by applying the classification methodology on previously filtered and unfiltered images, comparing the results. A method of cluster detections was also presented, based on density-based spatial clustering of applications with noise (DBSCAN), which would make it possible, for example, to guide inspection actions and allow the calculation of the intensity of exploitation (IEX). Although the differences between the tested models were in the order of less than 5%, the tests on the RGB composition (where R = coefficient of variation; G = minimum values; and B = gradient) presented a slightly better performance compared to the others in terms of the number of correct classifications for selective logging, in particular using the model Painters (accuracy = 92%) even in the generalization tests, which presented an overall accuracy of 87%, and in the test on RGB from the unfiltered image pair (accuracy of 90%). These results indicate that multitemporal X-band SAR data have the potential for monitoring selective logging in tropical forests, especially in combination with CNN techniques.
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Tillaguango B, Alvarado R, Dagar V, Murshed M, Pinzón Y, Méndez P. Convergence of the ecological footprint in Latin America: the role of the productive structure. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2021; 28:59771-59783. [PMID: 34146332 DOI: 10.1007/s11356-021-14745-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Accepted: 06/01/2021] [Indexed: 06/12/2023]
Abstract
The ecological footprint is an indicator used in recent years to assess the accelerated process of environmental degradation experienced by countries. In particular, in regions or countries with high biodiversity, strong dependence on natural resources, high informality, and low productive specialization. We employ a set of quantitative techniques within the theoretical framework of convergence and found the existence of per capita ecological footprint clubs. In addition, we estimate a set of logistic regressions to obtain the average marginal effect of the determinants of the convergence clubs. We found that economic complexity, the shadow economy, and natural resources significantly influence the formation of convergence clubs. The results indicate the existence of convergence in three clubs, while two clubs are divergent. Environmental policymakers in this region should collectively mitigate environmental degradation and achieve sustainable development goals, particularly among the countries that make up the convergence clubs.
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Affiliation(s)
- Brayan Tillaguango
- Esai Business School, Universidad Espíritu Santo, 091650, Samborondon, Ecuador
| | - Rafael Alvarado
- Carrera de Economía and Centro de Investigaciones Sociales y Económicas, Universidad Nacional de Loja, 110150, Loja, Ecuador.
| | - Vishal Dagar
- Amity School of Economics, Amity University, Noida, Uttar Pradesh, India
| | - Muntasir Murshed
- School of Business and Economics, North South University, Dhaka, Bangladesh
| | - Yajaira Pinzón
- Carrera de Economía, Universidad Nacional de Loja, 110150, Loja, Ecuador
| | - Priscila Méndez
- Departamento de Economía, Universidad Católica del Norte, 1240000, Antofagasta, Chile
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