Project Funding: 2017 - 2020

NRA: 2016 NASA: Carbon Monitoring System   

Funded by NASA

Abstract:
Rationale: Mexico is a high-biodiversity country with nearly 40% of its territory forested. During the last decade carbon cycle science efforts have rapidly increased, and state-of- the-art measurements on carbon (C) stocks, dynamics, and forest architecture are available at representative landscapes and at the national level. Mexico is now recognized to be one of the few non-Annex I countries capable of implementing Reducing Emissions from Deforestation and Forest Degradation plus improving forest management, carbon stock enhancement and conservation (REDD+). This proposal builds on previous NASA CMS efforts to improve monitoring, reporting and verification (MRV) for implementation of REDD+ in Mexico. Furthermore, this proposal takes advantage of other NASA CMS efforts to develop algorithms and apply high performance computing (HPC) approaches to develop a framework for estimating high-resolution (30 m resolution) carbon-related estimates at national scales. Combining CMS efforts and experiences are important to (a) increase interoperability across CMS products, (b) test their applicability and uncertainty, (c) identify their strengths and areas for improvements, and (d) move to higher Application Readiness Levels (ARLs). Mexico can be considered a “data rich” country, and this proposal is an opportunity to develop, test, and improve the applicability of different NASA CMS products across North America. The goal of this proposal is to: improve a national carbon monitoring framework to synthetize forest inventory and remote sensing information, while increasing spatial resolution and knowledge to provide support for implementation of REDD+ across Mexico. Specific objectives: 1) Harmonize available data to increase interoperability and synthesis efforts; 2) Build multi-scale resolution products at the national level (1km to 30 m); 3) Develop high-resolution estimates (15 and 1 m) at intensive monitoring sites; and 4) Collaborate with stakeholders to improve a national carbon monitoring framework where information is available to support research and management/policy decisions. Approach: This proposal builds upon ongoing efforts supported by NASA, the USDA Forest Service (supported by USAID), the Mexican Carbon Program, and multiple institutions represented by participants in this proposal. This proposal will a) harmonize and synthetize available national information to increase data interoperability for synthesis studies, and development/validation of CMS products; b) build multi-scale resolution products (between 1 km to 30 m) of forest cover change, aboveground biomass, forest structural variables (e.g., tree height), soil carbon, and gross primary productivity (GPP) with associate uncertainties at the national level; and c) generate a framework for high-resolution (15 m to 1 m) estimates of aboveground biomass, forest structural variables, soil carbon, and GPP across a network of intensive monitoring sites. These efforts will be supported by already available data sets (site level and national level), NASA-derived remote sensing information, and using the NASA Earth Exchange (NEX) HPC framework. Significance: This proposal supports NASA research through a) validation and improvement of CMS-related applications; b) advancement of remote sensing-based approaches to MRV; c) supporting implementation of REDD+ projects; d) building synergy and collaboration between different NASA CMS efforts; and f) working with scientists and stakeholders to increase ARLs and transfer CMS efforts and products across North America.

Publications:

2020. State of the Climate in 2019. Bulletin of the American Meteorological Society. 101(8), S1-S429. DOI: 10.1175/2020BAMSStateoftheClimate.1

Barba, J., Cueva, A., Bahn, M., Barron-Gafford, G. A., Bond-Lamberty, B., Hanson, P. J., Jaimes, A., Kulmala, L., Pumpanen, J., Scott, R. L., Wohlfahrt, G., Vargas, R. 2018. Comparing ecosystem and soil respiration: Review and key challenges of tower-based and soil measurements. Agricultural and Forest Meteorology. 249, 434-443. DOI: 10.1016/j.agrformet.2017.10.028

Basu, S., Mukhopadhyay, S., Karki, M., DiBiano, R., Ganguly, S., Nemani, R., Gayaka, S. 2018. Deep neural networks for texture classification--A theoretical analysis. Neural Networks. 97, 173-182. DOI: 10.1016/j.neunet.2017.10.001

Bond-Lamberty, B., Bailey, V. L., Chen, M., Gough, C. M., Vargas, R. 2018. Globally rising soil heterotrophic respiration over recent decades. Nature. 560(7716), 80-83. DOI: 10.1038/s41586-018-0358-x

Cueva, A., Bullock, S. H., Mendez-Alonzo, R., Lopez-Reyes, E., Vargas, R. 2021. Foliage Senescence as a Key Parameter for Modeling Gross Primary Productivity in a Mediterranean Shrubland. Journal of Geophysical Research: Biogeosciences. 126(1). DOI: 10.1029/2020JG005839

Delgado-Balbuena J, Yépez EA, Paz-Pellat F, Ángeles-Pérez G, Aguirre-Gutiérrez C, Alvarado-Barrientos MS, Arredondo T, Ayala-Niño F, Bullock S, Castellanos AE, Cueva A, Figueroa-Espinoza B, Garatuza- Payán J, González-del Castillo E, González-Sosa E, Guevara-Escobar A, Hinojo-Hinojo C, Kyaw-Tha PU, Lizárraga-Celaya C, Maya-Delgado Y, Oechel W, Pérez-Ruiz ER, Quesada-Avendaño M, Robles-Zazueta CA, Rodríguez JC, Rojas-Robles NE, Tarin-Terrazas T, Troyo-Diéguez E, Uuh-Sonda J, Vargas-Terminel ML, Vargas R, Vega-Puga MG, Verduzco VS, Vivoni ER, Watts CJ (2019) Database of vertical carbon dioxide fluxes at terrestrial and coastal ecosystems in Mexico. Elementos para Politicas Publicas. 2(2)93-108. http://www.elementospolipub.org/ojs/index.php/epp/article/view/41/49

Delgado-Balbuena, J., Arredondo, J. T., Loescher, H. W., Pineda-Martinez, L. F., Carbajal, J. N., Vargas, R. 2019. Seasonal Precipitation Legacy Effects Determine the Carbon Balance of a Semiarid Grassland. Journal of Geophysical Research: Biogeosciences. 124(4), 987-1000. DOI: 10.1029/2018JG004799

Ganguly S, Basu S, Nemani R, Mukhopadhyay S, Michaelis A, Votava P, Milesi C, Kumar U (2018) Deep Learning for Very High-Resolution Imagery Classification, Large-Scale Machine Learning in the Earth Sciences, Chapter 7, in Large-Scale Machine Learning in the Earth Sciences. Srivastava, A.N., Nemani, R. and Steinhaeuser, K. eds., CRC Press. ISBN: 9781498703888.

Guevara, M., Arroyo, C., Brunsell, N., Cruz, C. O., Domke, G., Equihua, J., Etchevers, J., Hayes, D., Hengl, T., Ibelles, A., Johnson, K., Jong, B., Libohova, Z., Llamas, R., Nave, L., Ornelas, J. L., Paz, F., Ressl, R., Schwartz, A., Victoria, A., Wills, S., Vargas, R. 2020. Soil Organic Carbon Across Mexico and the Conterminous United States (1991-2010). Global Biogeochemical Cycles. 34(3). DOI: 10.1029/2019GB006219

Guevara, M., Olmedo, G. F., Stell, E., Yigini, Y., Aguilar Duarte, Y., Arellano Hernandez, C., Arevalo, G. E., Arroyo-Cruz, C. E., Bolivar, A., Bunning, S., Bustamante Canas, N., Cruz-Gaistardo, C. O., Davila, F., Dell Acqua, M., Encina, A., Figueredo Tacona, H., Fontes, F., Hernandez Herrera, J. A., Ibelles Navarro, A. R., Loayza, V., Manueles, A. M., Mendoza Jara, F., Olivera, C., Osorio Hermosilla, R., Pereira, G., Prieto, P., Ramos, I. A., Rey Brina, J. C., Rivera, R., Rodriguez-Rodriguez, J., Roopnarine, R., Rosales Ibarra, A., Rosales Riveiro, K. A., Schulz, G. A., Spence, A., Vasques, G. M., Vargas, R. R., Vargas, R. 2018. No silver bullet for digital soil mapping: country-specific soil organic carbon estimates across Latin America. SOIL. 4(3), 173-193. DOI: 10.5194/soil-4-173-2018

Guevara, M., Vargas, R. 2021. Prediccion de carbono organico en los suelos de Mexico a 1 m de profundidad y 90 m de resolucion espacial (1999-2009). REVISTA TERRA LATINOAMERICANA. 39. DOI: 10.28940/terra.v39i0.1241

Harden, J. W., Hugelius, G., Ahlstrom, A., Blankinship, J. C., Bond-Lamberty, B., Lawrence, C. R., Loisel, J., Malhotra, A., Jackson, R. B., Ogle, S., Phillips, C., Ryals, R., Todd-Brown, K., Vargas, R., Vergara, S. E., Cotrufo, M. F., Keiluweit, M., Heckman, K. A., Crow, S. E., Silver, W. L., DeLonge, M., Nave, L. E. 2017. Networking our science to characterize the state, vulnerabilities, and management opportunities of soil organic matter. Global Change Biology. 24(2). DOI: 10.1111/gcb.13896

Hashimoto, H., Wang, W., Melton, F. S., Moreno, A. L., Ganguly, S., Michaelis, A. R., Nemani, R. R. 2019. High-resolution mapping of daily climate variables by aggregating multiple spatial data sets with the random forest algorithm over the conterminous United States. International Journal of Climatology. 39(6), 2964-2983. DOI: 10.1002/joc.5995

Hayes, D. J., Vargas, R., Alin, S., Conant, R. T., Hutyra, L. R., Jacobson, A. R., Kurz, W. A., Liu, S., McGuire, A. D., Poulter, B., Woodall, C. W. 2018. Chapter 2: The North American Carbon Budget. Second State of the Carbon Cycle Report DOI: 10.7930/SOCCR2.2018.Ch2

Hinojo-Hinojo, C., Castellanos, A. E., Huxman, T., Rodriguez, J. C., Vargas, R., Romo-Leon, J. R., Biederman, J. A. 2019. Native shrubland and managed buffelgrass savanna in drylands: Implications for ecosystem carbon and water fluxes. Agricultural and Forest Meteorology. 268, 269-278. DOI: 10.1016/j.agrformet.2019.01.030

Hinojo-Hinojo, C., Castellanos, A. E., Llano-Sotelo, J., Penuelas, J., Vargas, R., Romo-Leon, J. R. 2018. High Vcmax, Jmax and photosynthetic rates of Sonoran Desert species: Using nitrogen and specific leaf area traits as predictors in biochemical models. Journal of Arid Environments. 156, 1-8. DOI: 10.1016/j.jaridenv.2018.04.006

Jian, J., Vargas, R., Anderson-Teixeira, K., Stell, E., Herrmann, V., Horn, M., Kholod, N., Manzon, J., Marchesi, R., Paredes, D., Bond-Lamberty, B. 2021. A restructured and updated global soil respiration database (SRDB-V5). Earth System Science Data. 13(2), 255-267. DOI: 10.5194/essd-13-255-2021

Kim, M., Ham, B., Kraxner, F., Shvidenko, A., Schepaschenko, D., Krasovskii, A., Park, T., Lee, W. 2020. Species- and elevation-dependent productivity changes in East Asian temperate forests. Environmental Research Letters. 15(3), 034012. DOI: 10.1088/1748-9326/ab71a2

Kumar, U., Ganguly, S., Nemani, R. R., Raja, K. S., Milesi, C., Sinha, R., Michaelis, A., Votava, P., Hashimoto, H., Li, S., Wang, W., Kalia, S., Gayaka, S. 2017. Exploring Subpixel Learning Algorithms for Estimating Global Land Cover Fractions from Satellite Data Using High Performance Computing. Remote Sensing. 9(11), 1105. DOI: 10.3390/rs9111105

Liu, Q., Basu, S., Ganguly, S., Mukhopadhyay, S., DiBiano, R., Karki, M., Nemani, R. 2019. DeepSat V2: feature augmented convolutional neural nets for satellite image classification. Remote Sensing Letters. 11(2), 156-165. DOI: 10.1080/2150704X.2019.1693071

Peano, D., Hemming, D., Materia, S., Delire, C., Fan, Y., Joetzjer, E., Lee, H., Nabel, J. E. M. S., Park, T., Peylin, P., Warlind, D., Wiltshire, A., Zaehle, S. Plant phenology evaluation of CRESCENDO land surface models - Part I: start and end of growing season DOI: 10.5194/bg-2020-319

Piao, S., Wang, X., Park, T., Chen, C., Lian, X., He, Y., Bjerke, J. W., Chen, A., Ciais, P., Tommervik, H., Nemani, R. R., Myneni, R. B. 2019. Characteristics, drivers and feedbacks of global greening. Nature Reviews Earth & Environment. 1(1), 14-27. DOI: 10.1038/s43017-019-0001-x

Rojas-Robles, N. E., Garatuza-Payan, J., Alvarez-Yepiz, J. C., Sanchez-Mejia, Z. M., Vargas, R., Yepez, E. A. 2020. Environmental Controls on Carbon and Water Fluxes in an Old-Growth Tropical Dry Forest. Journal of Geophysical Research: Biogeosciences. 125(8). DOI: 10.1029/2020JG005666

Saatchi, S., Longo, M., Xu, L., Yang, Y., Abe, H., Andre, M., Aukema, J. E., Carvalhais, N., Cadillo-Quiroz, H., Cerbu, G. A., Chernela, J. M., Covey, K., Sanchez-Clavijo, L. M., Cubillos, I. V., Davies, S. J., De Sy, V., De Vleeschouwer, F., Duque, A., Sybille Durieux, A. M., De Avila Fernandes, K., Fernandez, L. E., Gammino, V., Garrity, D. P., Gibbs, D. A., Gibbon, L., Gowae, G. Y., Hansen, M., Lee Harris, N., Healey, S. P., Hilton, R. G., Johnson, C. M., Kankeu, R. S., Laporte-Goetz, N. T., Lee, H., Lovejoy, T., Lowman, M., Lumbuenamo, R., Malhi, Y., Albert Martinez, J. M., Nobre, C., Pellegrini, A., Radachowsky, J., Roman, F., Russell, D., Sheil, D., Smith, T. B., Spencer, R. G., Stolle, F., Tata, H. L., Torres, D. D. C., Tshimanga, R. M., Vargas, R., Venter, M., West, J., Widayati, A., Wilson, S. N., Brumby, S., Elmore, A. C. 2021. Detecting vulnerability of humid tropical forests to multiple stressors. One Earth. 4(7), 988-1003. DOI: 10.1016/j.oneear.2021.06.002

Soriano-Luna, M., Angeles-Perez, G., Guevara, M., Birdsey, R., Pan, Y., Vaquera-Huerta, H., Valdez-Lazalde, J., Johnson, K., Vargas, R. 2018. Determinants of Above-Ground Biomass and Its Spatial Variability in a Temperate Forest Managed for Timber Production. Forests. 9(8), 490. DOI: 10.3390/f9080490

Stell, E., Warner, D., Jian, J., Bond-Lamberty, B., Vargas, R. 2021. Spatial biases of information influence global estimates of soil respiration: How can we improve global predictions? Global Change Biology. 27(16), 3923-3938. DOI: 10.1111/gcb.15666

Vandal, T., Kodra, E., Dy, J., Ganguly, S., Nemani, R., Ganguly, A. R. 2018. Quantifying Uncertainty in Discrete-Continuous and Skewed Data with Bayesian Deep Learning. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. DOI: 10.1145/3219819.3219996

Vandal, T., Kodra, E., Ganguly, S., Michaelis, A., Nemani, R., Ganguly, A. R. 2017. DeepSD. Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. DOI: 10.1145/3097983.3098004

Vazquez-Lule A*, Bejarano M, Olguin M, Villeda E, Vargas R (2018). Integración y síntesis de datos para el monitoreo de los manglares de México. In: Métodos para la caracterización de los manglares mexicanos: un enfoque espacial multi-escala. Edited by CONABIO. pp 245-265. ISBN: 978-607-8570-03-4. http://www.biodiversidad.gob.mx/ecosistemas/manglares2013/pdf/metodos/caracterizacion_manglares.pdf

Vazquez-Lule, A., Colditz, R., Herrera-Silveira, J., Guevara, M., Rodriguez-Zuniga, M. T., Cruz, I., Ressl, R., Vargas, R. 2019. Greenness trends and carbon stocks of mangroves across Mexico. Environmental Research Letters. 14(7), 075010. DOI: 10.1088/1748-9326/ab246e

Villarreal, S., Guevara, M., Alcaraz-Segura, D., Brunsell, N. A., Hayes, D., Loescher, H. W., Vargas, R. 2018. Ecosystem functional diversity and the representativeness of environmental networks across the conterminous United States. Agricultural and Forest Meteorology. 262, 423-433. DOI: 10.1016/j.agrformet.2018.07.016

Villarreal, S., Guevara, M., Alcaraz-Segura, D., Vargas, R. 2019. Optimizing an Environmental Observatory Network Design Using Publicly Available Data. Journal of Geophysical Research: Biogeosciences. 124(7), 1812-1826. DOI: 10.1029/2018JG004714

Villarreal, S., Vargas, R. 2021. Representativeness of FLUXNET Sites Across Latin America. Journal of Geophysical Research: Biogeosciences. 126(3). DOI: 10.1029/2020JG006090

Warner, D. L., Bond-Lamberty, B., Jian, J., Stell, E., Vargas, R. 2019. Spatial Predictions and Associated Uncertainty of Annual Soil Respiration at the Global Scale. Global Biogeochemical Cycles. 33(12), 1733-1745. DOI: 10.1029/2019GB006264

Wheeler, K. I., Levia, D. F., Vargas, R. 2019. Visible and near-infrared hyperspectral indices explain more variation in lower-crown leaf nitrogen concentrations in autumn than in summer. Oecologia. 192(1), 13-27. DOI: 10.1007/s00442-019-04554-2

More details may be found in the following project profile(s):

• NASA Terrestrial Ecology (TE) Project Profile
• North American Carbon Program (NACP) Project Profile
• NASA Carbon Monitoring System (CMS) Project Profile