The National Forestry Commission of Mexico continuously monitors forest structure within the country’s continental territory by the implementation of the National Forest and Soils Inventory (INFyS). Due to the challenges involved in collecting data exclusively from field surveys, there are spatial information gaps for important forest attributes. This can produce bias or increase uncertainty when generating estimates required to support forest management decisions. Our objective is to predict the spatial distribution of tree height and tree density in all Mexican forests. We performed wall-to-wall spatial predictions of both attributes in 1-km grids, using ensemble machine learning across each forest type in Mexico. Predictor variables include remote sensing imagery and other geospatial data (e.g., vegetation indexes, surface temperature). Training data is from the 2009-2014 cycle (n>26,000 sampling plots). Spatial cross validation suggested that the model had a better performance when predicting tree height r2=0.4 [0.15,0.55] (mean[min, max]) than for tree density r2=0.2[0.10,0.31]. Maximum values of tree height were for coniferous forests, coniferous-broadleaf forests and cloud mountain forest (~36 m, 30 m and 21 m, respectively). Tropical forests had maximum values of tree density (~1370 trees/ha), followed by tropical dry forest (1006 trees/ha) and coniferous forest (988 trees/ha). Although most forests had relatively low values of uncertainty, e.g., values <40%, arid and semiarid ecosystems had high uncertainty in both tree height and tree density predictions, e.g., values >60%. The applied open science approach we present is easily replicable and scalable, thus it is helpful to assist in the decision-making and future of the National Forest and Soils Inventory. This work highlights the need for technical capabilities aimed to use and resignify all the effort done by the Mexican Forestry Commission in implementing the INFyS.

Climate mitigation planning requires accurate information on forest carbon dynamics. Forest carbon monitoring and modeling systems need to step beyond the traditional Monitoring, Reporting, and Verification (MRV) framework of current forest cover and carbon stock. They should be able to project potential future carbon stocks with high accuracy and high spatial resolution over large policy-relevant spatial domains. Previous efforts have demonstrated the possibility and value of combining a process-based ecosystem model (Ecosystem Demography, ED), high-resolution (1-meter) lidar and NAIP data, field inventory data, and meteorology and soil properties in a prototype carbon monitoring and modeling system developed for the state of Maryland. Here we present recent work on expanding the Maryland prototype to a 10x larger domain, namely the Regional Greenhouse Gas Initiative (RGGI+) domain consisting of the states of Maryland, Delaware, Pennsylvania, New York, New Jersey, Rhode Island, Connecticut, Massachusetts, Vermont, New Hampshire, and Maine. The system expansion includes an updated version of the ED ecosystem model, improved initialization strategy, and expanded Cal/val approach. High-resolution wall-to-wall maps of current aboveground carbon, carbon sequestration potential, carbon sequestration potential gap, and time to reach sequestration potential are provided at 90m resolution across the RGGI+ domain. Total forest aboveground carbon sequestration potential gap is estimated to be over 2,300 Tg C for the RGGI+ region, about 1.5 times of contemporary aboveground carbon stock. States and counties exhibit variations in carbon sequestration potential gap, implying different policy planning for future afforestation/reforestation and forest conservation activities. Here we present the details of this new carbon monitoring and modeling system as well as regional results, including evaluations of our estimates against USFS Forest Inventory and Analysis (FIA) data, multiple wall-to-wall AGB maps, and state-wide and county-wide future carbon sequestration potential over time.

International frameworks for climate mitigation that build from national actions have been developed under the United National Framework Convention on Climate Change and advanced most recently through the Paris Climate Agreement. In parallel, sub-national actors have set greenhouse gas (GHG) reduction goals and developed corresponding climate mitigation plans. Within the U.S., multi-state coalitions have formed to facilitate coordination of related science and policy. Here, utilizing the forum of the NASA Carbon Monitoring System’s Multi-State Working Group (MSWG), we collected and reviewed climate mitigation plans for 11 states in the Regional Greenhouse Gas Initiative (RGGI) region of the Eastern U.S. For each state we reviewed the 1) policy framework for climate mitigation, 2) GHG reduction goals, 3) inclusion of forest carbon in the state’s climate action plan, 4) existing science used to estimate forest carbon, and 5) stated needs for carbon monitoring science. Across the region, we found important differences across all categories. While all states have GHG reduction goals and framework documents, nearly three-quarters of all states do not account for forest carbon when planning GHG reductions; those that do account for forest carbon use a variety of scientific methods with various levels of planning detail and guidance. We suggest that a common, efficient, standardized forest carbon monitoring system would provide important benefits to states and the geographic region as a whole. In addition, such a system would allow for more effective transparency and progress tracking to support state, national, and international efforts to increase ambition and implementation of climate goals.