1
|
Zhang-Zheng H, Adu-Bredu S, Duah-Gyamfi A, Moore S, Addo-Danso SD, Amissah L, Valentini R, Djagbletey G, Anim-Adjei K, Quansah J, Sarpong B, Owusu-Afriyie K, Gvozdevaite A, Tang M, Ruiz-Jaen MC, Ibrahim F, Girardin CAJ, Rifai S, Dahlsjö CAL, Riutta T, Deng X, Sun Y, Prentice IC, Oliveras Menor I, Malhi Y. Contrasting carbon cycle along tropical forest aridity gradients in West Africa and Amazonia. Nat Commun 2024; 15:3158. [PMID: 38605006 PMCID: PMC11009382 DOI: 10.1038/s41467-024-47202-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2023] [Accepted: 03/22/2024] [Indexed: 04/13/2024] Open
Abstract
Tropical forests cover large areas of equatorial Africa and play a substantial role in the global carbon cycle. However, there has been a lack of biometric measurements to understand the forests' gross and net primary productivity (GPP, NPP) and their allocation. Here we present a detailed field assessment of the carbon budget of multiple forest sites in Africa, by monitoring 14 one-hectare plots along an aridity gradient in Ghana, West Africa. When compared with an equivalent aridity gradient in Amazonia, the studied West African forests generally had higher productivity and lower carbon use efficiency (CUE). The West African aridity gradient consistently shows the highest NPP, CUE, GPP, and autotrophic respiration at a medium-aridity site, Bobiri. Notably, NPP and GPP of the site are the highest yet reported anywhere for intact forests. Widely used data products substantially underestimate productivity when compared to biometric measurements in Amazonia and Africa. Our analysis suggests that the high productivity of the African forests is linked to their large GPP allocation to canopy and semi-deciduous characteristics.
Collapse
Affiliation(s)
- Huanyuan Zhang-Zheng
- Environmental Change Institute, School of Geography and the Environment, University of Oxford, Oxford, United Kingdom.
- Leverhulme Centre for Nature Recovery, University of Oxford, Oxford, United Kingdom.
| | - Stephen Adu-Bredu
- Forestry Research Institute of Ghana, Council for Scientific and Industrial Research, Kumasi, Ghana
- Department of Natural Resources Management, CSIR College of Science and Technology, Kumasi, Ghana
| | - Akwasi Duah-Gyamfi
- Forestry Research Institute of Ghana, Council for Scientific and Industrial Research, Kumasi, Ghana
| | - Sam Moore
- Environmental Change Institute, School of Geography and the Environment, University of Oxford, Oxford, United Kingdom
| | - Shalom D Addo-Danso
- Forestry Research Institute of Ghana, Council for Scientific and Industrial Research, Kumasi, Ghana
| | - Lucy Amissah
- Forestry Research Institute of Ghana, Council for Scientific and Industrial Research, Kumasi, Ghana
| | | | - Gloria Djagbletey
- Forestry Research Institute of Ghana, Council for Scientific and Industrial Research, Kumasi, Ghana
| | - Kelvin Anim-Adjei
- Forestry Research Institute of Ghana, Council for Scientific and Industrial Research, Kumasi, Ghana
| | - John Quansah
- Forestry Research Institute of Ghana, Council for Scientific and Industrial Research, Kumasi, Ghana
| | - Bernice Sarpong
- Forestry Research Institute of Ghana, Council for Scientific and Industrial Research, Kumasi, Ghana
| | - Kennedy Owusu-Afriyie
- Forestry Research Institute of Ghana, Council for Scientific and Industrial Research, Kumasi, Ghana
| | - Agne Gvozdevaite
- Environmental Change Institute, School of Geography and the Environment, University of Oxford, Oxford, United Kingdom
| | - Minxue Tang
- Georgina Mace Centre for the Living Planet, Department of Life Sciences, Imperial College London, Silwood Park Campus, Buckhurst Road, Ascot, United Kingdom
| | - Maria C Ruiz-Jaen
- Forestry Division, Food and Agriculture Organization of the United Nations, Panama City, Panama
| | - Forzia Ibrahim
- Department of Forest Ecology, Faculty of Forestry and Wood Sciences, Czech University of Life Sciences, Praha, Czech Republic
| | - Cécile A J Girardin
- Environmental Change Institute, School of Geography and the Environment, University of Oxford, Oxford, United Kingdom
| | - Sami Rifai
- School of Biological Sciences, University of Adelaide, Adelaide, South Australia, Australia
| | - Cecilia A L Dahlsjö
- Environmental Change Institute, School of Geography and the Environment, University of Oxford, Oxford, United Kingdom
| | - Terhi Riutta
- Environmental Change Institute, School of Geography and the Environment, University of Oxford, Oxford, United Kingdom
| | - Xiongjie Deng
- Environmental Change Institute, School of Geography and the Environment, University of Oxford, Oxford, United Kingdom
| | - Yuheng Sun
- Groningen Institute for Evolutionary Life Sciences, University of Groningen, P.O. Box 11103, 9700 CC, Groningen, The Netherlands
| | - Iain Colin Prentice
- Georgina Mace Centre for the Living Planet, Department of Life Sciences, Imperial College London, Silwood Park Campus, Buckhurst Road, Ascot, United Kingdom
- Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing, China
| | - Imma Oliveras Menor
- Environmental Change Institute, School of Geography and the Environment, University of Oxford, Oxford, United Kingdom
- AMAP (Botanique et Modelisation de l'Architecture des Plantes et des Végétations), CIRAD, CNRS, INRA, IRD,Université de Montpellier, Montpellier, France
| | - Yadvinder Malhi
- Environmental Change Institute, School of Geography and the Environment, University of Oxford, Oxford, United Kingdom.
- Leverhulme Centre for Nature Recovery, University of Oxford, Oxford, United Kingdom.
| |
Collapse
|
2
|
The Sentinel-3 OLCI Terrestrial Chlorophyll Index (OTCI): Algorithm Improvements, Spatiotemporal Consistency and Continuity with the MERIS Archive. REMOTE SENSING 2020. [DOI: 10.3390/rs12162652] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The Ocean and Land Colour Instrument (OLCI) on-board Sentinel-3 (2016–present) was designed with similar mechanical and optical characteristics to the Envisat Medium Resolution Imaging Spectrometer (MERIS) (2002–2012) to ensure continuity with a number of land and marine biophysical products. The Sentinel-3 OLCI Terrestrial Chlorophyll Index (OTCI) is an indicator of canopy chlorophyll content and is intended to continue the legacy of the Envisat MERIS Terrestrial Chlorophyll Index (MTCI). Despite spectral similarities, validation and verification of consistency is essential to inform the user community about the product’s accuracy, uncertainty, and fitness for purpose. This paper aims to: (i) describe the theoretical basis of the Sentinel-3 OTCI and (ii) evaluate the spatiotemporal consistency between the Sentinel-3 OTCI and the Envisat MTCI. Two approaches were used to conduct the evaluation. Firstly, agreement between the Sentinel-3 OTCI and the Envisat MTCI archive was assessed over the Committee for Earth Observation Satellites (CEOS) Land Product Validation (LPV) core validation sites, enabling the temporal consistency of the two products to be investigated. Secondly, intercomparison of monthly Level-3 Sentinel-3 OTCI and Envisat MTCI composites was carried out to evaluate the spatial distribution of differences across the globe. In both cases, the agreement was quantified with statistical metrics (R2, NRMSD, bias) using an Envisat MTCI climatology based on the MERIS archive as the reference. Our results demonstrate strong agreement between the products. Specifically, high 1:1 correspondence (R2 >0.88), low global mean percentage difference (−1.86 to 0.61), low absolute bias (<0.1), and minimal error (NRMSD ~0.1) are observed. The temporal profiles reveal consistency in the expected range of values, amplitudes, and seasonal trajectories. Biases and discrepancies may be attributed to changes in land management practices, land cover change, and extreme climatic events occurred during the time gap between the missions; however, this requires further investigation. This research confirms that Sentinel-3 OTCI dataset can be used along with the Envisat MTCI to provide a data coverage over the last 20 years.
Collapse
|
3
|
Varghese R, Behera MD. Annual and seasonal variations in gross primary productivity across the agro-climatic regions in India. ENVIRONMENTAL MONITORING AND ASSESSMENT 2019; 191:631. [PMID: 31520222 DOI: 10.1007/s10661-019-7796-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2019] [Accepted: 08/27/2019] [Indexed: 06/10/2023]
Abstract
Gross primary productivity (GPP) is a vital ecosystem variable that is used as a proxy to study the functional behaviour of a terrestrial ecosystem and its ability to regulate atmospheric CO2 by working as a carbon pool. India, having the potential terrestrial ecosystem dynamics to absorb the atmospheric carbon dioxide to some extent, is one of the least-explored regions in terms of carbon monitoring studies. The current study evaluates the applicability of a newly developed, quantum yield-based, remote sensing data-driven diagnostic model called the Southampton Carbon Flux (SCARF). This model was used to estimate the annual and seasonal variability of the terrestrial GPP over the Indian region with a spatial resolution of 1 km during 2008. This modified version of the conventional production efficiency model successfully predicted GPP using meteorological variables (PAR, air temperature and dew point temperature), the fraction of photosynthetically active radiation and quantum yield of C3 and C4 plants as the key input parameters. The annual GPP values were in the range from 0 to 4147.55 g C m-2 year-1, with a mean value of 1507.32 g C m-2 year-1. The maximum and minimum GPP were during the summer monsoon and pre-monsoon, respectively. The seasonal and annual distributions of GPP over the study area obtained using the SCARF model, and the MODIS GPP product (MOD17A2H) were similar. However, MODIS was found to underestimate the GPP in all regions and an overestimation in eastern Himalaya region. The study reveals that environmental scalars, specifically water stress, are the pivotal controlling variables responsible for the variation of GPP in India. The estimates of the GPP in different regions of the study area were made using SCARF, and an eddy covariance technique was similar. The SCARF model can be used to estimate GPP on a global scale. SCARF appears to be a better model in terms of the simplicity of the algorithm, performance and resolution. Thus, it may give higher accuracy in carbon monitoring studies.
Collapse
Affiliation(s)
- Roma Varghese
- Centre for Oceans, Rivers, Atmosphere and Land Sciences (CORAL), Indian Institute of Technology Kharagpur, Kharagpur, India.
| | - M D Behera
- Centre for Oceans, Rivers, Atmosphere and Land Sciences (CORAL), Indian Institute of Technology Kharagpur, Kharagpur, India
| |
Collapse
|