Wayne Walker

Mapping and monitoring carbon stocks with satellite observations: a comparison of methods.

Mapping and monitoring carbon stocks in forested regions of the world, particularly the tropics, has attracted a great deal of attention in recent years as deforestation and forest degradation account for up to 30% of anthropogenic carbon emissions, and are now included in climate change negotiations. We review the potential for satellites to measure carbon stocks, specifically aboveground biomass (AGB), and provide an overview of a range of approaches that have been developed and used to map AGB across a diverse set of conditions and geographic areas. We provide a summary of types of remote sensing measurements relevant to mapping AGB, and assess the relative merits and limitations of each. We then provide an overview of traditional techniques of mapping AGB based on ascribing field measurements to vegetation or land cover type classes, and describe the merits and limitations of those relative to recent data mining algorithms used in the context of an approach based on direct utilization of remote sensing measurements, whether optical or lidar reflectance, or radar backscatter. We conclude that while satellite remote sensing has often been discounted as inadequate for the task, attempts to map AGB without satellite imagery are insufficient. Moreover, the direct remote sensing approach provided more coherent maps of AGB relative to traditional approaches. We demonstrate this with a case study focused on continental Africa and discuss the work in the context of reducing uncertainty for carbon monitoring and markets.

Large-Area Classification and Mapping of Forest and Land Cover in the Brazilian Amazon: A Comparative Analysis of ALOS/PALSAR and Landsat Data Sources

Information on the distribution of tropical forests is critical to decision-making on a host of globally significant issues ranging from climate stabilization and biodiversity conservation to poverty reduction and human health. The majority of tropical nations need high-resolution, satellite-based maps of their forests as the international community now works to craft an incentive-based mechanism to compensate tropical nations for maintaining their forests intact. The effectiveness of such a mechanism will depend in large part on the capacity of current and near-future Earth observation satellites to provide information that meets the requirements of international monitoring protocols now being discussed. Here we assess the ability of a state-of-the-art satellite radar sensor, the ALOS/PALSAR, to support large-area land cover classification as well as high-resolution baseline mapping of tropical forest cover. Through a comprehensive comparative analysis involving twenty separate PALSAR- and Landsat-based classifications, we confirm the potential of PALSAR as an accurate (>90%) source for spatially explicit estimates of forest cover based on data and analyses from a large and diverse region encompassing the Xingu River headwaters in southeastern Amazonia. Pair-wise spatial comparisons among maps derived from PALSAR, Landsat, and PRODES, the Brazilian Amazon deforestation monitoring program, revealed a high degree of spatial similarity. Given that a long-term data record consisting of current and future spaceborne radar sensors is now expected, our results point to the important role that spaceborne imaging radar can play in complementing optical remote sensing to enable the design of robust forest monitoring systems.

Land cover dynamics following a deforestation ban in northern Costa Rica

Forest protection policies potentially reduce deforestation and re-direct agricultural expansion to already-cleared areas. Using satellite imagery, we assessed whether deforestation for conversion to pasture and cropland decreased in the lowlands of northern Costa Rica following the 1996 ban on forest clearing, despite a tripling of area under pineapple cultivation in the last decade. We observed that following the ban, mature forest loss decreased from 2.2% to 1.2% per year, and the proportion of pineapple and other export-oriented cropland derived from mature forest declined from 16.4% to 1.9%. The post-ban expansion of pineapples and other crops largely replaced pasture, exotic and native tree plantations, and secondary forests. Overall, there was a small net gain in forest cover due to a shifting mosaic of regrowth and clearing in pastures, but cropland expansion decreased reforestation rates. We conclude that forest protection efforts in northern Costa Rica have likely slowed mature forest loss and succeeded in re-directing expansion of cropland to areas outside mature forest. Our results suggest that deforestation bans may protect mature forests better than older forest regrowth and may restrict clearing for large-scale crops more effectively than clearing for pasture.

Tropical forests are a net carbon source based on aboveground measurements of gain and loss

Forests out of balance Are tropical forests a net source or net sink of atmospheric carbon dioxide? As fundamental a question as that is, there still is no agreement about the answer, with different studies suggesting that it is anything from a sizable sink to a modest source. Baccini et al. used 12 years of MODIS satellite data to determine how the aboveground carbon density of woody, live vegetation has changed throughout the entire tropics on an annual basis. They find that the tropics are a net carbon source, with losses owing to deforestation and reductions in carbon density within standing forests being double that of gains resulting from forest growth. Science, this issue p. 230 Tropical forests release more CO2 to the atmosphere than they remove from it. The carbon balance of tropical ecosystems remains uncertain, with top-down atmospheric studies suggesting an overall sink and bottom-up ecological approaches indicating a modest net source. Here we use 12 years (2003 to 2014) of MODIS pantropical satellite data to quantify net annual changes in the aboveground carbon density of tropical woody live vegetation, providing direct, measurement-based evidence that the world’s tropical forests are a net carbon source of 425.2 ± 92.0 teragrams of carbon per year (Tg C year–1). This net release of carbon consists of losses of 861.7 ± 80.2 Tg C year–1 and gains of 436.5 ± 31.0 Tg C year–1. Gains result from forest growth; losses result from deforestation and from reductions in carbon density within standing forests (degradation or disturbance), with the latter accounting for 68.9% of overall losses.

Land-use-driven stream warming in southeastern Amazonia

Large-scale cattle and crop production are the primary drivers of deforestation in the Amazon today. Such land-use changes can degrade stream ecosystems by reducing connectivity, changing light and nutrient inputs, and altering the quantity and quality of streamwater. This study integrates field data from 12 catchments with satellite-derived information for the 176 000 km2 upper Xingu watershed (Mato Grosso, Brazil). We quantify recent land-use transitions and evaluate the influence of land management on streamwater temperature, an important determinant of habitat quality in small streams. By 2010, over 40 per cent of catchments outside protected areas were dominated (greater than 60% of area) by agriculture, with an estimated 10 000 impoundments in the upper Xingu. Streams in pasture and soya bean watersheds were significantly warmer than those in forested watersheds, with average daily maxima over 4°C higher in pasture and 3°C higher in soya bean. The upstream density of impoundments and riparian forest cover accounted for 43 per cent of the variation in temperature. Scaling up, our model suggests that management practices associated with recent agricultural expansion may have already increased headwater stream temperatures across the Xingu. Although increased temperatures could negatively impact stream biota, conserving or restoring riparian buffers could reduce predicted warming by as much as fivefold.

Forest carbon in Amazonia: the unrecognized contribution of indigenous territories and protected natural areas

Carbon sequestration is a widely acknowledged and increasingly valued function of tropical forest ecosystems; however, until recently, the information needed to assess the carbon storage capacity of Amazonian indigenous territories (ITs) and protected natural areas (PNAs) in a global context remained either lacking or out of reach. Here, as part of a novel north–south collaboration among Amazonian indigenous and non-governmental organization (NGO) networks, scientists and policy experts, we show that the nine-nation network of nearly 3000 ITs and PNAs stores more carbon above ground than all of the Democratic Republic of the Congo and Indonesia combined, and, despite the ostensibly secure status of these cornerstones of Amazon conservation, a conservative risk assessment considering only ongoing and planned development projects puts nearly 20% of this carbon at risk, encompassing an area of tropical forest larger than that found in Colombia, Ecuador and Peru combined. International recognition of and renewed investment in these globally vital landscapes are therefore critical to ensuring their continued contribution to maintaining cultural identity, ecosystem integrity and climate stability.

Measurement and monitoring needs, capabilities and potential for addressing reduced emissions from deforestation and forest degradation under REDD+

This paper presents an overview of the state of measurement and monitoring capabilities for forests in the context of REDD+ needs, with a focus on what is currently possible, where improvements are needed, and what capabilities will be advanced in the near-term with new technologies already under development. We summarize the role of remote sensing (both satellite and aircraft) for observational monitoring of forests, including measuring changes in their current and past extent for setting baselines, their carbon stock density for estimating emissions in areas that are deforested or degraded, and their regrowth dynamics following disturbance. We emphasize the synergistic role of integrating field inventory measurements with remote sensing for best practices in monitoring, reporting and verification. We also address the potential of remote sensing for enforcing safeguards on conservation of natural forests and biodiversity. We argue that capabilities exist now to meet operational needs for REDD+ measurement, reporting, and verification and reference levels. For some other areas of importance for REDD+, such as safeguards for natural forests and biodiversity, monitoring capabilities are approaching operational in the near term. For all REDD+ needs, measurement capabilities will rapidly advance in the next few years as a result of new technology as well as advances in capacity building both within and outside of the tropical forest nations on which REDD+ is primarily focused.

The role of science in Reducing Emissions from Deforestation and Forest Degradation (REDD)

Emissions of carbon from tropical deforestation and degradation currently account for 12–15% of total anthropogenic carbon emissions each year, and Reducing Emissions from Deforestation and Forest Degradation (REDD; including REDD+) is poised to be the primary international mechanism with the potential to reduce these emissions. This article provides a brief summary of the scientific research that led to REDD, and that continues to help refine and resolve issues of effectiveness, efficiency and equitability for a REDD mechanism. However, REDD deals only with tropical forests and there are other regions, ecosystems and processes that govern the sources and sinks of carbon in terrestrial ecosystems. Ongoing research will reveal which of these other flows of carbon are most important, and which of them might present further opportunities to reduce emissions (or enhance sinks) through environmental policy mechanisms, as well as how they might do this.

Estimating Aboveground Biomass in Tropical Forests: Field Methods and Error Analysis for the Calibration of Remote Sensing Observations

Mapping and monitoring of forest carbon stocks across large areas in the tropics will necessarily rely on remote sensing approaches, which in turn depend on field estimates of biomass for calibration and validation purposes. Here, we used field plot data collected in a tropical moist forest in the central Amazon to gain a better understanding of the uncertainty associated with plot-level biomass estimates obtained specifically for the calibration of remote sensing measurements. In addition to accounting for sources of error that would be normally expected in conventional biomass estimates (e.g., measurement and allometric errors), we examined two sources of uncertainty that are specific to the calibration process and should be taken into account in most remote sensing studies: the error resulting from spatial disagreement between field and remote sensing measurements (i.e., co-location error), and the error introduced when accounting for temporal differences in data acquisition. We found that the overall uncertainty in the field biomass was typically 25% for both secondary and primary forests, but ranged from 16 to 53%. Co-location and temporal errors accounted for a large fraction of the total variance (>65%) and were identified as important targets for reducing uncertainty in studies relating tropical forest biomass to remotely sensed data. Although measurement and allometric errors were relatively unimportant when considered alone, combined they accounted for roughly 30% of the total variance on average and should not be ignored. Our results suggest that a thorough understanding of the sources of error associated with field-measured plot-level biomass estimates in tropical forests is critical to determine confidence in remote sensing estimates of carbon stocks and fluxes, and to develop strategies for reducing the overall uncertainty of remote sensing approaches.

Modeling Height, Biomass, and Carbon in U.S. Forests from FIA, SRTM, and Ancillary National Scale Data Sets

A U.S. national scale modeling effort is underway to map height, biomass and carbon layers for the Year 2000 exploiting synergies among recently developed national-scale data sets derived from Shuttle Radar Topography Mission (SRTM) and Landsat ETM data. Due to the short radar wavelengths used for the SRTM mission (C-band and X-band), the SRTM InSAR signal represents a reflective surface rather than the ground elevation whenever vegetation or anthropogenic features are present. By differencing ground elevations from SRTM elevations over vegetated terrain a spatially continuous height signal, the mean height of the scattering phase center (MHSPC), can be extracted which is correlated with true vegetation canopy height. The MHSPC is dependent on the scattering characteristics of the observed canopy which are largely a function of vegetation cover type, density, and phenological state. Given a ground measured reference dataset of vegetation canopy height, the SRTM MHSPC signal can then be used with information on cover type and density to spatially estimate vegetation canopy height with empirically developed regression models. Subsequently, given spatial estimates of vegetation canopy height in conjunction with information on vegetation cover type and density, aboveground dry biomass can be modeled as well. Finally, spatial data layers of carbon distribution can be calculated with established aboveground dry biomass to carbon conversion factors. Within the conterminous United States, a timely confluence of spatial data sets provides the framework for the development of empirical regression models and their application at a national scale. In addition to the SRTM mission, national-scale data sets of ground surface elevation (i.e., National Elevation Dataset, NED), existing vegetation type (i.e., LANDFIRE) and canopy density (i.e., National Land Cover Database, NLCD 2001,) are currently being produced for the circa 2000 timeframe. These data sets are complemented by a database of ca. 150,000 forest plots provided by the USDA Forest Service Forest Inventory and Analysis (FIA) program. A novel approach based on objectoriented image analysis is employed to address SRTM inherent noise characteristics and to enhance the accuracy of the InSAR signal. As a statistical modeling framework a regression tree based approach is employed. A prototype study in central Utah covering 62,000 km (i.e., NLCD 2001 mapping zone 16), which contains a diversity of vegetation types is now completed. Based on data from 508 FIA field plots, overall vegetation canopy height and aboveground dry biomass estimates at r (and absolute error) values of 0.78 (2.1 m) and 0.56 (24 tons/ha) were obtained. The NBCD 2000 project is scheduled for completion in late 2008. Data will be accessible at 30 m postings via the U.S. Geological Survey seamless data server as mapping zones are completed.