A National, Detailed Map of Forest Aboveground Carbon Stocks in Mexico

Cartus, Oliver; Kellndorfer, Josef; Walker, Wayne; Franco, Carol; Bishop, Jesse; Santos, Lucio Andrés; Fuentes, José María Michel

doi:10.3390/rs6065559

Cited by 110 publications

(132 citation statements)

References 85 publications

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“…The inclusion of additional predictor layers (e.g., Landsat TC, SRTM DEM) with SAR products improves AGB estimations and they achieve the highest accuracy (in terms of R 2 and RMSE) when all predictors are used for all three scenarios: i.e., using original INFyS, BFAST-filtered and randomly filtered data ( Figure 5). These results based on the multi-sensor combination are in agreement with previous studies, e.g., [23,25,27]. …”

Section: Resultssupporting

confidence: 83%

“…The accuracy of AGB estimates based on original data is consistent with the results produced by the authors of [25] in terms of statistical metrics (R 2 = 0.52 [25] vs. R 2 = 0.55 (mean for Campeche and Comillas); RMSE = 28.2 t/ha [25] vs. RMSE = 23.8 t/ha (mean for Campeche and Comillas)), while AGB estimates based on BFAST-filtered plots produce more accurate estimates.…”

Section: Discussionsupporting

confidence: 80%

“…The estimation of vegetation parameters (e.g., AGB, vegetation height, growing stock volume) can be improved by the fusing of SAR imagery with optical data (e.g., from Landsat) and complementary information such as altitude [23][24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Improved Multi-Sensor Satellite-Based Aboveground Biomass Estimation by Selecting Temporally Stable Forest Inventory Plots Using NDVI Time Series

Urbazaev

Thiel

Migliavacca

et al. 2016

Forests

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Accurate estimates of aboveground biomass (AGB) are crucial to assess terrestrial C-stocks and C-emissions as well as to develop sustainable forest management strategies. In this study we used Synthetic Aperture Radar (SAR) data acquired at L-band and the Landsat tree cover product together with Moderate Resolution Image Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) time series data to improve AGB estimations over two study areas in southern Mexico. We used Mexican National Forest Inventory (INFyS) data collected between 2005 and 2011 to calibrate AGB models as well as to validate the derived AGB products. We applied MODIS NDVI time series data analysis to exclude field plots in which abrupt changes were detected. For this, we used Breaks For Additive Seasonal and Trend analysis (BFAST). We modelled AGB using an original field dataset and BFAST-filtered data. The results show higher accuracies of AGB estimations using BFAST-filtered data than using original field data in terms of R 2 and root mean square error (RMSE) for both dry and humid tropical forests of southern Mexico. The best results were found in areas with high deforestation rates where the AGB models based on the BFAST-filtered data substantially outperformed those based on original field data (R 2 BFAST = 0.62 vs. R 2 orig = 0.45; RMSE BFAST = 28.4 t/ha vs. RMSE orig = 33.8 t/ha). We conclude that the presented method shows great potential to improve AGB estimations and can be easily and automatically implemented over large areas.

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Section: Resultssupporting

confidence: 83%

Section: Discussionsupporting

confidence: 80%

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Improved Multi-Sensor Satellite-Based Aboveground Biomass Estimation by Selecting Temporally Stable Forest Inventory Plots Using NDVI Time Series

Urbazaev

Thiel

Migliavacca

et al. 2016

Forests

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“…These could lead to C content overestimations in studied Mexican forests in the order of 1.5 Mg ha -1 (Yerena-Yamallel et al 2012b) or 3.9 Mg ha -1 (Aguirre-Calderón & Jiménez-Pérez 2011). Taxa-specific results contribute to a more realistic assumptions of C sinks in different ecosystems (Cartus et al 2014, Matula et al 2015 with implications for other countries where the studied species occur and challenges the current assumption of a 50% C concentration of plant tissues across forests worldwide.…”

Section: Discussionmentioning

confidence: 99%

Tissue carbon concentration of 175 Mexican forest species

Pompa-García¹,

Rodríguez²,

Jurado³

et al. 2017

iForest

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Reliable calculations of carbon stocks in forest ecosystems are crucial for proper implementation of global warming mitigation policies. Accurate estimations depend upon applying the correct factor of carbon (C) concentration for different forest species and tissues instead of the often assumed 50% carbon content. Despite the high forest species richness in Mexico and the increasing CO2 emissions, data on carbon concentrations in forest plant tissues are scarce. In this study, we determined variation in C concentration of different tissues for 175 plant species common in Mexican forests. C contents were estimated and contrasted for plant distribution, taxa, and plant structure (main stems, branches, twigs, bark, leaves, buds, fruits, roots and root cuticles). The mean C concentration across species was 44.7%. Species significantly differed in C concentration by tissue, environment and taxa. These multi-species data contribute to improve precision on estimates of C balance in terrestrial ecosystems, reducing the uncertainty in C inventories in Mexico and elsewhere.

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“…Accuracy tests used correlation coefficient (r) and Root Mean Square Error (RMSE) [14], [15], [16], [17], [5]. Equation :…”

Section: Test Accuracy With Rmsementioning

confidence: 99%

Estimation of Mangrove Carbon Stock With Hybrid Method Using Image Sentinel-2

Muhsoni¹

2018

GEOMATE

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ABSTRACT:Field survey data combined with remote sensing data were an ideal and practical method for estimating carbon stocks. The objective of this research was to get an estimation model of mangrove carbon stock with good accuracy. Modeling used hybrid methods, by combining satellite image analysis and field data. The result of this research was to get the mangrove carbon estimation model. Model 1 merging between NNIP vegetation index equation using regression of power/geometry and six variables multiple regression (NDRE or WVVI vegetation index, sediment depth, soil density,% C soil depth 0-15 cm, 15-50 cm and >50 cm). RMSE test resulted 0.4778 t 100 m -2 and % RMSE 16.12%. Model 2 NNIP vegetation index and three variable regression (VIRRE vegetation index, sediment depth, soil density). RMSE test resulted 0.5639 t 100 m -2 and % RMSE 19.03%. Model 3 uses NNIP vegetation index and two variable regression (NDRE vegetation index and sediment depth). RMSE test resulted 0.7295 t 100 m -2 and RMSE % 24.63%. Model 4 incorporation of NNIP vegetation index and multiple regression of 3 variables (VIRRE vegetation index, average sediment depth value 100.63 cm, soil density value 1.02 g cm -3 ). RMSE test resulted 1.0043 t 100 m -2 and % RMSE 33.89%.

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A National, Detailed Map of Forest Aboveground Carbon Stocks in Mexico

Cited by 110 publications

References 85 publications

Improved Multi-Sensor Satellite-Based Aboveground Biomass Estimation by Selecting Temporally Stable Forest Inventory Plots Using NDVI Time Series

Improved Multi-Sensor Satellite-Based Aboveground Biomass Estimation by Selecting Temporally Stable Forest Inventory Plots Using NDVI Time Series

Tissue carbon concentration of 175 Mexican forest species

Estimation of Mangrove Carbon Stock With Hybrid Method Using Image Sentinel-2

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