Monitoring tropical forest carbon stocks and emissions using Planet satellite data

AbstractTropical forests are crucial for mitigating climate change, but many forests continue to be driven from carbon sinks to sources through human activities. To support more sustainable forest uses, we need to measure and monitor carbon stocks and emissions at high spatial and temporal resolution. We developed the first large-scale very high-resolution map of aboveground carbon stocks and emissions for the country of Peru by combining 6.7 million hectares of airborne LiDAR measurements of top-of-canopy height with thousands of Planet Dove satellite images into a random forest machine learning regression workflow, obtaining an R2 of 0.70 and RMSE of 25.38 Mg C ha−1 for the nationwide estimation of aboveground carbon density (ACD). The diverse ecosystems of Peru harbor 6.928 Pg C, of which only 2.9 Pg C are found in protected areas or their buffers. We found significant carbon emissions between 2012 and 2017 in areas aggressively affected by oil palm and cacao plantations, agricultural and urban expansions or illegal gold mining. Creating such a cost-effective and spatially explicit indicators of aboveground carbon stocks and emissions for tropical countries will serve as a transformative tool to quantify the climate change mitigation services that forests provide.

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Modelling above-ground carbon dynamics using multi-temporal airborne lidar: insights from a Mediterranean woodland

Biogeosciences ◽

10.5194/bg-13-961-2016 ◽

2016 ◽

Vol 13 (4) ◽

pp. 961-973 ◽

Cited By ~ 17

Author(s):

W. Simonson ◽

P. Ruiz-Benito ◽

F. Valladares ◽

D. Coomes

Keyword(s):

Climate Change ◽

Large Scale ◽

Critical Role ◽

Carbon Fluxes ◽

Carbon Dynamics ◽

Airborne Lidar ◽

Lidar Data ◽

Low Carbon ◽

Data Set ◽

Multi Temporal

Abstract. Woodlands represent highly significant carbon sinks globally, though could lose this function under future climatic change. Effective large-scale monitoring of these woodlands has a critical role to play in mitigating for, and adapting to, climate change. Mediterranean woodlands have low carbon densities, but represent important global carbon stocks due to their extensiveness and are particularly vulnerable because the region is predicted to become much hotter and drier over the coming century. Airborne lidar is already recognized as an excellent approach for high-fidelity carbon mapping, but few studies have used multi-temporal lidar surveys to measure carbon fluxes in forests and none have worked with Mediterranean woodlands. We use a multi-temporal (5-year interval) airborne lidar data set for a region of central Spain to estimate above-ground biomass (AGB) and carbon dynamics in typical mixed broadleaved and/or coniferous Mediterranean woodlands. Field calibration of the lidar data enabled the generation of grid-based maps of AGB for 2006 and 2011, and the resulting AGB change was estimated. There was a close agreement between the lidar-based AGB growth estimate (1.22 Mg ha−1 yr−1) and those derived from two independent sources: the Spanish National Forest Inventory, and a tree-ring based analysis (1.19 and 1.13 Mg ha−1 yr−1, respectively). We parameterised a simple simulator of forest dynamics using the lidar carbon flux measurements, and used it to explore four scenarios of fire occurrence. Under undisturbed conditions (no fire) an accelerating accumulation of biomass and carbon is evident over the next 100 years with an average carbon sequestration rate of 1.95 Mg C ha−1 yr−1. This rate reduces by almost a third when fire probability is increased to 0.01 (fire return rate of 100 years), as has been predicted under climate change. Our work shows the power of multi-temporal lidar surveying to map woodland carbon fluxes and provide parameters for carbon dynamics models. Space deployment of lidar instruments in the near future could open the way for rolling out wide-scale forest carbon stock monitoring to inform management and governance responses to future environmental change.

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Airborne lidar measurements of ozone, water vapor, and aerosols in large-scale atmospheric studies

Proceedings of IGARSS '93 - IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.1993.322200 ◽

2002 ◽

Author(s):

E.V. Browell

Keyword(s):

Water Vapor ◽

Large Scale ◽

Airborne Lidar ◽

Lidar Measurements

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Estimating the Aboveground Carbon Density of Coniferous Forests by Combining Airborne LiDAR and Allometry Models at Plot Level

Frontiers in Plant Science ◽

10.3389/fpls.2019.00917 ◽

2019 ◽

Vol 10 ◽

Cited By ~ 2

Author(s):

Hongke Hao ◽

Weizhong Li ◽

Xuan Zhao ◽

Qingrui Chang ◽

Pengxiang Zhao

Keyword(s):

Airborne Lidar ◽

Coniferous Forests ◽

Carbon Density ◽

Aboveground Carbon ◽

Aboveground Carbon Density

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Modeling Forest Aboveground Carbon Density in the Brazilian Amazon with Integration of MODIS and Airborne LiDAR Data

Remote Sensing ◽

10.3390/rs12203330 ◽

2020 ◽

Vol 12 (20) ◽

pp. 3330

Author(s):

Xiandie Jiang ◽

Guiying Li ◽

Dengsheng Lu ◽

Emilio Moran ◽

Mateus Batistella

Keyword(s):

Carbon Stock ◽

Brazilian Amazon ◽

Airborne Lidar ◽

Coefficient Of Determination ◽

Lidar Data ◽

Carbon Density ◽

Modis Data ◽

Aboveground Carbon ◽

Aboveground Carbon Density ◽

Airborne Lidar Data

Timely updates of carbon stock distribution are needed to better understand the impacts of deforestation and degradation on forest carbon stock dynamics. This research aimed to explore an approach for estimating aboveground carbon density (ACD) in the Brazilian Amazon through integration of MODIS (moderate resolution imaging spectroradiometer) and a limited number of light detection and ranging (Lidar) data samples using linear regression (LR) and random forest (RF) algorithms, respectively. Airborne LiDAR data at 23 sites across the Brazilian Amazon were collected and used to calculate ACD. The ACD estimation model, which was developed by Longo et al. in the same study area, was used to map ACD distribution in the 23 sites. The LR and RF methods were used to develop ACD models, in which the samples extracted from LiDAR-estimated ACD were used as dependent variables and MODIS-derived variables were used as independent variables. The evaluation of modeling results indicated that ACD can be successfully estimated with a coefficient of determination of 0.67 and root mean square error of 4.18 kg C/m2 using RF based on spectral indices. The mixed pixel problem in MODIS data is a major factor in ACD overestimation, while cloud contamination and data saturation are major factors in ACD underestimation. These uncertainties in ACD estimation using MODIS data make it difficult to examine annual ACD dynamics of degradation and growth, however this method can be used to examine the deforestation-induced ACD loss.

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Evidence of large scale ozone depletion within the Arctic Polar Vortex 94/95 based on airborne LIDAR measurements

Geophysical Research Letters ◽

10.1029/96gl00772 ◽

1996 ◽

Vol 23 (8) ◽

pp. 813-816 ◽

Cited By ~ 32

Author(s):

M. Wirth ◽

W. Renger

Keyword(s):

Ozone Depletion ◽

Large Scale ◽

Polar Vortex ◽

Airborne Lidar ◽

The Arctic ◽

Lidar Measurements

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High-resolution Mapping of Forest Carbon Stocks in the Colombian Amazon

Biogeosciences Discussions ◽

10.5194/bgd-9-2445-2012 ◽

2012 ◽

Vol 9 (3) ◽

pp. 2445-2479 ◽

Cited By ~ 14

Author(s):

G. P. Asner ◽

J. K. Clark ◽

J. Mascaro ◽

G. A. Galindo García ◽

K. D. Chadwick ◽

...

Keyword(s):

High Resolution ◽

Tropical Forest ◽

Large Scale ◽

Carbon Stocks ◽

Forest Carbon ◽

Airborne Lidar ◽

High Resolution Mapping ◽

Resolution Mapping ◽

Carbon Mapping ◽

Colombian Amazon

Abstract. High-resolution mapping of tropical forest carbon stocks can assist forest management and improve implementation of large-scale carbon retention and enhancement programs. Previous high-resolution approaches have relied on field plot and/or Light Detection and Ranging (LiDAR) samples of aboveground carbon density, which are typically upscaled to larger geographic areas using stratification maps. Such efforts often rely on detailed vegetation maps to stratify the region for sampling, but existing tropical forest maps are often too coarse and field plots too sparse for high resolution carbon assessments. We developed a top-down approach for high-resolution carbon mapping in a 16.5 million ha region (>40 %) of the Colombian Amazon – a remote landscape seldom documented. We report on three advances for large-scale carbon mapping: (i) employing a universal approach to airborne LiDAR-calibration with limited field data; (ii) quantifying environmental controls over carbon densities; and (iii) developing stratification- and regression-based approaches for scaling up to regions outside of LiDAR coverage. We found that carbon stocks are predicted by a combination of satellite-derived elevation, fractional canopy cover and terrain ruggedness, allowing upscaling of the LiDAR samples to the full 16.5 million ha region. LiDAR-derived carbon mapping samples had 14.6 % uncertainty at 1 ha resolution, and regional maps based on stratification and regression approaches had 25.6 % and 29.6 % uncertainty, respectively, in any given hectare. High-resolution approaches with reported local-scale uncertainties will provide the most confidence for monitoring changes in tropical forest carbon stocks. Improved confidence will allow resource managers and decision-makers to more rapidly and effectively implement actions that better conserve and utilize forests in tropical regions.

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Spatially-Explicit Testing of a General Aboveground Carbon Density Estimation Model in a Western Amazonian Forest Using Airborne LiDAR

Remote Sensing ◽

10.3390/rs8010009 ◽

2015 ◽

Vol 8 (1) ◽

pp. 9 ◽

Cited By ~ 10

Author(s):

Patricio Molina ◽

Gregory Asner ◽

Mercedes Farjas Abadía ◽

Juan Ojeda Manrique ◽

Luis Sánchez Diez ◽

...

Keyword(s):

Density Estimation ◽

Airborne Lidar ◽

Spatially Explicit ◽

Carbon Density ◽

Estimation Model ◽

Amazonian Forest ◽

Aboveground Carbon ◽

Aboveground Carbon Density

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Mature Andean forests as globally important carbon sinks and future carbon refuges

Nature Communications ◽

10.1038/s41467-021-22459-8 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Alvaro Duque ◽

Miguel A. Peña ◽

Francisco Cuesta ◽

Sebastián González-Caro ◽

Peter Kennedy ◽

...

Keyword(s):

Climate Change ◽

Carbon Dynamics ◽

Carbon Stocks ◽

Biotic Factors ◽

Tropical Andes ◽

Size Dependent ◽

The Past ◽

Aboveground Carbon ◽

Abiotic And Biotic Factors ◽

Global Carbon

AbstractIt is largely unknown how South America’s Andean forests affect the global carbon cycle, and thus regulate climate change. Here, we measure aboveground carbon dynamics over the past two decades in 119 monitoring plots spanning a range of >3000 m elevation across the subtropical and tropical Andes. Our results show that Andean forests act as strong sinks for aboveground carbon (0.67 ± 0.08 Mg C ha−1 y−1) and have a high potential to serve as future carbon refuges. Aboveground carbon dynamics of Andean forests are driven by abiotic and biotic factors, such as climate and size-dependent mortality of trees. The increasing aboveground carbon stocks offset the estimated C emissions due to deforestation between 2003 and 2014, resulting in a net total uptake of 0.027 Pg C y−1. Reducing deforestation will increase Andean aboveground carbon stocks, facilitate upward species migrations, and allow for recovery of biomass losses due to climate change.

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Modelling above-ground carbon dynamics using multi-temporal airborne lidar: insights from a Mediterranean woodland

Biogeosciences Discussions ◽

10.5194/bgd-12-14739-2015 ◽

2015 ◽

Vol 12 (17) ◽

pp. 14739-14772 ◽

Cited By ~ 1

Author(s):

W. Simonson ◽

P. Ruiz-Benito ◽

F. Valladares ◽

D. Coomes

Keyword(s):

Climate Change ◽

Large Scale ◽

Critical Role ◽

Carbon Fluxes ◽

Carbon Dynamics ◽

National Forest Inventory ◽

Airborne Lidar ◽

Fire Occurrence ◽

Low Carbon ◽

Multi Temporal

Abstract. Woodlands represent highly significant carbon sinks globally, though could lose this function under future climatic change. Effective large-scale monitoring of these woodlands has a critical role to play in mitigating for, and adapting to, climate change. Mediterranean woodlands have low carbon densities, but represent important global carbon stocks due to their extensiveness and are particularly vulnerable because the region is predicted to become much hotter and drier over the coming century. Airborne lidar is already recognized as an excellent approach for high-fidelity carbon mapping, but few studies have used multi-temporal lidar surveys to measure carbon fluxes in forests and none have worked with Mediterranean woodlands. We use a multi-temporal (five year interval) airborne lidar dataset for a region of central Spain to estimate above-ground biomass (AGB) and carbon dynamics in typical mixed broadleaved/coniferous Mediterranean woodlands. Field calibration of the lidar data enabled the generation of grid-based maps of AGB for 2006 and 2011, and the resulting AGB change were estimated. There was a close agreement between the lidar-based AGB growth estimate (1.22 Mg ha−1 year−1) and those derived from two independent sources: the Spanish National Forest Inventory, and a~tree-ring based analysis (1.19 and 1.13 Mg ha−1 year−1, respectively). We parameterised a simple simulator of forest dynamics using the lidar carbon flux measurements, and used it to explore four scenarios of fire occurrence. Under undisturbed conditions (no fire occurrence) an accelerating accumulation of biomass and carbon is evident over the next 100 years with an average carbon sequestration rate of 1.95 Mg C ha−1 year−1. This rate reduces by almost a third when fire probability is increased to 0.01, as has been predicted under climate change. Our work shows the power of multi-temporal lidar surveying to map woodland carbon fluxes and provide parameters for carbon dynamics models. Space deployment of lidar instruments in the near future could open the way for rolling out wide-scale forest carbon stock monitoring to inform management and governance responses to future environmental change.

Download Full-text