Faculty Opinions recommendation of Invasive grass reduces aboveground carbon stocks in shrublands of the Western US.

Abstract. Fire frequencies are changing in Neotropical savannas and forests as a result of forest fragmentation and increasing drought. Such changes in fire regime and climate are hypothesized to destabilize tropical carbon storage, but there has been little consideration of the widespread variability in tree fire tolerance strategies. To test how aboveground carbon stocks change with fire frequency and composition of plants with different fire tolerance strategies, we update the Ecosystem Demography model 2 (ED2) with (i) a fire survivorship module based on tree bark thickness (a key fire-tolerance trait across woody plants in savannas and forests), and (ii) plant functional types representative of trees in the region. With these updates, the model is better able to predict how fire frequency affects population demography and aboveground woody carbon. Simulations illustrate that the high survival rate of thick-barked, large trees reduces carbon losses with increasing fire frequency, with high investment in bark being particularly important in reducing losses in the wettest sites. Additionally, in landscapes that frequently burn, bark investment can broaden the range of climate and fire conditions under which savannas occur by reducing the range of conditions leading to either complete tree loss or complete grass loss. These results highlight that tropical vegetation dynamics depend not only on rainfall and changing fire frequencies but also on tree fire survival strategy. Further, our results indicate that fire survival strategy is fundamentally important in regulating tree size demography in ecosystems exposed to fire, which increases the preservation of aboveground carbon stocks and the coexistence of different plant functional groups.

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ABILITY OF LANDSAT-8 OLI DERIVED TEXTURE METRICS IN ESTIMATING ABOVEGROUND CARBON STOCKS OF COPPICE OAK FORESTS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xli-b8-751-2016 ◽

2016 ◽

Vol XLI-B8 ◽

pp. 751-754

Author(s):

A. Safari ◽

H. Sohrabi

Keyword(s):

Low Income ◽

Regression Models ◽

Accurate Method ◽

Carbon Stocks ◽

Low Income Countries ◽

Oak Forests ◽

Landsat 8 ◽

Zagros Mountains ◽

Aboveground Carbon ◽

Texture Metrics

The role of forests as a reservoir for carbon has prompted the need for timely and reliable estimation of aboveground carbon stocks. Since measurement of aboveground carbon stocks of forests is a destructive, costly and time-consuming activity, aerial and satellite remote sensing techniques have gained many attentions in this field. Despite the fact that using aerial data for predicting aboveground carbon stocks has been proved as a highly accurate method, there are challenges related to high acquisition costs, small area coverage, and limited availability of these data. These challenges are more critical for non-commercial forests located in low-income countries. Landsat program provides repetitive acquisition of high-resolution multispectral data, which are freely available. The aim of this study was to assess the potential of multispectral Landsat 8 Operational Land Imager (OLI) derived texture metrics in quantifying aboveground carbon stocks of coppice Oak forests in Zagros Mountains, Iran. We used four different window sizes (3×3, 5×5, 7×7, and 9×9), and four different offsets ([0,1], [1,1], [1,0], and [1,-1]) to derive nine texture metrics (angular second moment, contrast, correlation, dissimilar, entropy, homogeneity, inverse difference, mean, and variance) from four bands (blue, green, red, and infrared). Totally, 124 sample plots in two different forests were measured and carbon was calculated using species-specific allometric models. Stepwise regression analysis was applied to estimate biomass from derived metrics. Results showed that, in general, larger size of window for deriving texture metrics resulted models with better fitting parameters. In addition, the correlation of the spectral bands for deriving texture metrics in regression models was ranked as b4>b3>b2>b5. The best offset was [1,-1]. Amongst the different metrics, mean and entropy were entered in most of the regression models. Overall, different models based on derived texture metrics were able to explain about half of the variation in aboveground carbon stocks. These results demonstrated that Landsat 8 derived texture metrics can be applied for mapping aboveground carbon stocks of coppice Oak Forests in large areas.

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ESTIMATION OF ABOVEGROUND CARBON STOCK USING SAR SENTINEL-1 IMAGERY IN SAMARINDA CITY

International Journal of Remote Sensing and Earth Sciences (IJReSES) ◽

10.30536/j.ijreses.2021.v18.a3609 ◽

2021 ◽

Vol 18 (1) ◽

pp. 103

Author(s):

Bayu Elwanto Bagus Dewanto ◽

Retnadi Heru Jatmiko

Keyword(s):

Carbon Stock ◽

Open Space ◽

Exponential Model ◽

Carbon Stocks ◽

Total Carbon ◽

Empirical Modelling ◽

Aboveground Carbon ◽

Accuracy Test ◽

Estimated Error ◽

Total Carbon Stock

Estimation of aboveground carbon stock on stands vegetation, especially in green open space, has become an urgent issue in the effort to calculate, monitor, manage, and evaluate carbon stocks, especially in a massive urban area such as Samarinda City, Kalimantan Timur Province, Indonesia. The use of Sentinel-1 imagery was maximised to accommodate the weaknesses in its optical imagery, and combined with its ability to produce cloud-free imagery and minimal atmospheric influence. The study aims to test the accuracy of the estimated model of above-ground carbon stocks, to ascertain the total carbon stock, and to map the spatial distribution of carbon stocks on stands vegetation in Samarinda City. The methods used included empirical modelling of carbon stocks and statistical analysis comparing backscatter values and actual carbon stocks in the field using VV and VH polarisation. Model accuracy tests were performed using the standard error of estimate in independent accuracy test samples. The results show that Samarinda Utara subdistrict had the highest carbon stock of 3,765,255.9 tons in the VH exponential model. Total carbon stocks in the exponential VH models were 6,489,478.1 tons, with the highest maximum accuracy of 87.6 %, and an estimated error of 0.57 tons/pixel.

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Edaphic Conditions, Aboveground Carbon Stocks and Plant Diversity on Nickel Mine Tailings Dump Vegetated with Senegalia polyacantha (Willd.) Seigler & Ebinger

Land Degradation and Development ◽

10.1002/ldr.2696 ◽

2017 ◽

Vol 28 (5) ◽

pp. 1641-1651 ◽

Cited By ~ 3

Author(s):

Edeth Mukaro ◽

Innocent Wadzanayi Nyakudya ◽

Luke Jimu

Keyword(s):

Plant Diversity ◽

Mine Tailings ◽

Carbon Stocks ◽

Aboveground Carbon ◽

Edaphic Conditions

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Spatial Variation in Tree Density and Estimated Aboveground Carbon Stocks in Southern Africa

Forests ◽

10.3390/f7030057 ◽

2016 ◽

Vol 7 (12) ◽

pp. 57 ◽

Cited By ~ 4

Author(s):

Lulseged Tamene ◽

Powell Mponela ◽

Gudeta Sileshi ◽

Jiehua Chen ◽

Jérôme Tondoh

Keyword(s):

Spatial Variation ◽

Southern Africa ◽

Carbon Stocks ◽

Tree Density ◽

Aboveground Carbon

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Monitoring tropical forest carbon stocks and emissions using Planet satellite data

Scientific Reports ◽

10.1038/s41598-019-54386-6 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 18

Author(s):

Ovidiu Csillik ◽

Pramukta Kumar ◽

Joseph Mascaro ◽

Tara O’Shea ◽

Gregory P. Asner

Keyword(s):

Climate Change ◽

Large Scale ◽

Cost Effective ◽

Carbon Stocks ◽

Airborne Lidar ◽

Aboveground Carbon ◽

Lidar Measurements ◽

High Resolution Map ◽

Aboveground Carbon Density ◽

Planet Satellite

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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