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This paper focuses on the use of models for increasing the precision of estimators in large-area forest surveys. It is motivated by the increasing availability of remotely sensed data, which facilitates the development of models predicting the variables of interest in forest surveys. We present, review and compare three different estimation frameworks where models play a core role: model-assisted, model-based, and hybrid estimation. The first two are well known, whereas the third has only recently been introduced in forest surveys. Hybrid inference mixes designbased and model-based inference, since it relies on a probability sample of auxiliary data and a model predicting the target variable from the auxiliary data..We review studies on large-area forest surveys based on model-assisted, modelbased, and hybrid estimation, and discuss advantages and disadvantages of the approaches. We conclude that no general recommendations can be made about whether model-assisted, model-based, or hybrid estimation should be preferred. The choice depends on the objective of the survey and the possibilities to acquire appropriate field and remotely sensed data. We also conclude that modelling approaches can only be successfully applied for estimating target variables such as growing stock volume or biomass, which are adequately related to commonly available remotely sensed data, and thus purely field based surveys remain important for several important forest parameters. Keywords: Design-based inference, Model-assisted estimation, Model-based inference, Hybrid inference, National forest inventory, Remote sensing, Sampling

Although partial harvests are common in many forest types globally, there has been little assessment of the potential to map the intensity of these harvests using Landsat data. We modeled basal area removal and percent cover change in a study area in central Washington (northwestern USA) using biennial Landsat imagery and reference data from historical aerial photos and a system of inventory plots. First, we assessed the correlation of Landsat spectral bands and associated indices with measured levels of forest removal. The variables most closely associated with forest removal were the shortwave infrared (SWIR) bands (5 and 7) and those strongly influenced by SWIR reflectance (particularly Tasseled Cap Wetness, and the Disturbance Index). The band and indices associated with near-infrared reflectance (band 4, Tasseled Cap Greenness, and the Normalized Difference Vegetation Index) were only weakly correlated with degree of forest removal. Two regression-based methods of estimating forest loss were tested. The first, termed “state model differencing” (SMD), involves creating a model representing the relationship between inventory data from any date and corresponding, cross-normalized spectral data. This “state model” is then applied to imagery from two dates, with the difference between the two estimates taken as estimated change. The second approach, which we called “direct change modeling” (DCM), involves modeling forest structure changes as a single term using re-measured inventory data and spectral differences from corresponding image pairs. In a leave-one-out cross-validation process, DCM-derived estimates of harvest intensity had lower root mean square errors than SMD for both relative basal area change and relative cover change. The higher measured accuracy of DCM in this project must be weighed against several operational advantages of SMD relating to less restrictive reference data requirements and more specific resultant estimates of change.

Ecosystem structure and function are strongly affected by disturbance events, many of which in North America are associated with seasonal temperature extremes, wildfires, and tropical storms. This study was conducted to evaluate patterns in a 19-year record of global satellite observations of vegetation phenology from the advanced very high resolution radiometer (AVHRR) as a means to characterize major ecosystem disturbance events and regimes. The fraction absorbed of photosynthetically active radiation (FPAR) by vegetation canopies worldwide has been computed at a monthly time interval from 1982 to 2000 and gridded at a spatial resolution of 8–km globally. Potential disturbance events were identified in the FPAR time series by locating anomalously low values (FPAR-LO) that lasted longer than 12 consecutive months at any 8-km pixel. We can find verifiable evidence of numerous disturbance types across North America, including major regional patterns of cold and heat waves, forest fires, tropical storms, and large-scale forest logging. Summed over 19 years, areas potentially influenced by major ecosystem disturbances (one FPAR-LO event over the period 1982–2000) total to more than 766,000 km2. The periods of highest detection frequency were 1987–1989, 1995–1997, and 1999. Sub-continental regions of the Pacific Northwest, Alaska, and Central Canada had the highest proportion (>90%) of FPAR-LO pixels detected in forests, tundra shrublands, and wetland areas. The Great Lakes region showed the highest proportion (39%) of FPAR-LO pixels detected in cropland areas, whereas the western United States showed the highest proportion (16%) of FPAR-LO pixels detected in grassland areas. Based on this analysis, an historical picture is emerging of periodic droughts and heat waves, possibly coupled with herbivorous insect outbreaks, as among the most important causes of ecosystem disturbance in North America.

Landsat satellite data has become ubiquitous in regional-scale forest disturbance detection. The Tasseled Cap (TC) transformation for Landsat data has been used in several disturbance-mapping projects because of its ability to highlight relevant vegetation changes. We used an automated composite analysis procedure to test four multi-date variants of the TC transformation (called ‘‘data structures’’ here) in their ability to facilitate identification of stand-replacing disturbance. Data structures tested included one with all three TC indices (brightness, greenness, wetness), one with just brightness and greenness, one with just wetness, and one called the Disturbance Index (DI) which is a novel combination of the three TC indices. Data structures were tested in the St. Petersburg region of Russia and in two ecologically distinct regions of Washington State in the US. In almost all cases, the TC variants produced more accurate change classifications than multi-date stacks of the original Landsat reflectance data. In general, there was little overall difference between the TC-derived data structures. However, DI performed better than the others at the Russian study area, where slower succession rates likely produce the most durable disturbance signal. Also, at the highly productive western Washington site, where the disturbance signal is likely the most ephemeral, DI and wetness performed worse than the larger data structures when a longer monitoring interval was used (eight years between image acquisitions instead of four). This suggests that both local forest recovery rates and the re-sampling interval should be considered in choosing a Landsat transformation for use in stand-replacing disturbance detection.

Many tropical forest plantation projects, particularly those funded through governmental or development agencies are designed to provide secondary social or environmental benefits in addition to timber. The research reported here examined the ecological effects of establishing a teak (Tectona grandis) plantation on an abandoned pasture in southwestern Costa Rica. The 10-year-old plantation’s understory was evaluated using a nearby non-planted area as a baseline for potential local recruitment of native tree species. The native trees in the teak plantation were significantly less abundant, less diverse, and more restricted to the lower height classes than the trees in the abandoned pasture. Furthermore, trees with shrubby growth forms dominated the plantation’s understory, whereas larger species were more common in the unplanted abandoned pasture. Several aspects of teak’s biology may contribute to the species’ exclusion of native trees. The primary goal of nearly all forest plantations is the production of timber. However, for those managers secondarily interested in the goal of providing habitat for native forest species, teak appears to be a poor choice of crop tree.

Background National forest resource assessments and monitoring, commonly known as National Forest Inventories (NFI’s), constitute an important national information infrastructure in many countries. Methods This study presents details about developments of the NFI in China, including sampling and plot design, and the uses of alternative data sources, and specifically • reviews the evolution of the national forest inventory in China through the 20th and 21st centuries, with some reference to Europe and the US; • highlights the emergence of some common international themes: consistency of measurement; more efficient sampling designs; implementation of improved technology; expansion of the variables monitored; scientific transparency; • presents an example of how China’s expanding NFI exemplifies these global trends. Results Main results and important changes in China’s NFI are documented, both to support continued trend analysis and to provide data users with historical perspective. Conclusions New technologies and data needs ensure that the Chinese NFI, like the national inventories in other countries, will continue to evolve. Within the context of historical change and current conditions, likely directions for this evolution are suggested. peerReviewed

Background Lidar height data collected by the Geosciences Laser Altimeter System (GLAS) from 2002 to 2008 has the potential to form the basis of a globally consistent sample-based inventory of forest biomass. GLAS lidar return data were collected globally in spatially discrete full waveform “shots,” which have been shown to be strongly correlated with aboveground forest biomass. Relationships observed at spatially coincident field plots may be used to model biomass at all GLAS shots, and well-established methods of model-based inference may then be used to estimate biomass and variance for specific spatial domains. However, the spatial pattern of GLAS acquisition is neither random across the surface of the earth nor is it identifiable with any particular systematic design. Undefined sample properties therefore hinder the use of GLAS in global forest sampling. Results We propose a method of identifying a subset of the GLAS data which can justifiably be treated as a simple random sample in model-based biomass estimation. The relatively uniform spatial distribution and locally arbitrary positioning of the resulting sample is similar to the design used by the US national forest inventory (NFI). We demonstrated model-based estimation using a sample of GLAS data in the US state of California, where our estimate of biomass (211 Mg/hectare) was within the 1.4% standard error of the design-based estimate supplied by the US NFI. The standard error of the GLAS-based estimate was significantly higher than the NFI estimate, although the cost of the GLAS estimate (excluding costs for the satellite itself) was almost nothing, compared to at least US$ 10.5 million for the NFI estimate. Conclusions Global application of model-based estimation using GLAS, while demanding significant consolidation of training data, would improve inter-comparability of international biomass estimates by imposing consistent methods and a globally coherent sample frame. The methods presented here constitute a globally extensible approach for generating a simple random sample from the global GLAS dataset, enabling its use in forest inventory activities.

Population declines of early successional forest-associated wildlife species have been linked to declines in habitat abundance. Forest Inventory and Analysis (FIA) data can be used to estimate composition and change in 'young' forest, but such information typically lacks spatial specificity for determining landscape patterns that also affect habitat suitability. We developed a satellite remote sensing approach for making spatially explicit maps of young forest, using Landsat time series stacks and a vegetation change tracker (VCT) algorithm to identify forest disturbances occurring since 1985, and corresponding estimates of stand age based on year of disturbance. Wisconsin statewide estimates of young forest (1-20 years), from VCT and FIA, respectively, were 923,000 and 836,000 ha. Compared to FIA-based estimates, VCT-based estimates were statistically significantly higher for forest age classes of 1-5, 6-10, and 11-15 years, and significantly lower for the 16-20 year age class. Ongoing studies are examining causes and potential corrections for discrepancies between VCT- and FIA-based estimates of young forest in Wisconsin.

Incorporating ecological disturbance into biogeochemical models is critical for estimating current and future carbon stocks and fluxes. In particular, anthropogenic disturbances, such as forest conversion and wood harvest, strongly affect forest carbon dynamics within North America. This paper summarizes recent (2000.2008) rates of extraction, including both conversion and harvest, derived from national forest inventories for North America (the United States, Canada, and Mexico). During the 2000s, 6.1 million ha/yr were affected by harvest, another 1.0 million ha/yr were converted to other land uses through gross deforestation, and 0.4 million ha/yr were degraded. Thus about 1.0% of North America fs forests experienced some form of anthropogenic disturbance each year. However, due to harvest recovery, afforestation, and reforestation, the total forest area on the continent has been roughly stable during the decade. On average, about 110 m3 of roundwood volume was extracted per hectare harvested across the continent. Patterns of extraction vary among the three countries, with U.S. and Canadian activity dominated by partial and clear ]cut harvest, respectively, and activity in Mexico dominated by conversion (deforestation) for agriculture. Temporal trends in harvest and clearing may be affected by economic variables, technology, and forest policy decisions. While overall rates of extraction appear fairly stable in all three countries since the 1980s, harvest within the United States has shifted toward the southern United States and away from the Pacific Northwest.

Forest disturbances are thought to play a major role in controlling land-atmosphere fluxes of carbon. Under the auspices of the North American Carbon Program, the LEDAPS (Landsat Ecosystem Disturbance Adaptive Processing System) and NACP-FIA projects have been analyzing the Landsat satellite record to assess rates of forest disturbance across North America. In the LEDAPS project, wall-to-wall Landsat imagery for the period 1975-2000 has been converted to surface reflectance and analyzed for decadal losses (disturbance) or gains (regrowth) in biomass using a spectral "disturbance index". The NACP-FIA project relies on a geographic sample of dense Landsat image time series, allowing both disturbance rates and recovery trends to be characterized. Preliminary results for the 1990's indicate high rates of harvest within the southeastern US, Eastern Canada, and the Pacific Northwest, with spatially averaged (~50times50 km) turnover periods as low as 25-40 years. Lower rates of disturbance are found in the Rockies and Northeastern US.

[1] Knowledge of fuel load and composition is critical in fighting, preventing, and understanding wildfires. Commonly, the generation of fuel maps from remotely sensed imagery has made use of medium-resolution sensors such as Landsat. This paper presents a methodology to generate fuel type maps from high spatial resolution satellite data through object-oriented classification. Fuel maps were derived from QuickBird imagery, which offers a panchromatic and four multispectral bands ranging from 0.61 to 2.44 m resolution. The image used for this paper dated from July 2002 and is located in the NW region of Madrid, Spain. The Prometheus system, a fuel type classification adapted to the ecological characteristics of the European Mediterranean basin, was adopted for this study. Viewed with high-resolution imagery, fuel-related features are often aggregations of pixels exhibiting a variety of spectral properties. Correct identification and classification of these objects requires an explicit consideration of spatial context. We used an object-oriented approach, which allowed context consideration during the classification process, as a complement to traditional pixel-based methods. The map created with this approach was assessed to have greater than 80% accuracy for the prediction of six fuel classes. Results suggested that object-oriented classification of high-resolution imagery has the potential to create accurate and spatially precise fuel maps.

We monitored the status and trend of late-successional and old-growth forest (older forest) on 24 million ac of land managed by the Forest Service, Bureau of Land Management, and National Park Service in the Northwest Forest Plan (the Plan) area between 1994 and 2003. We developed baseline maps from satellite imagery of older forest conditions at the start of the Plan. We used remotely sensed change detection to track losses of older forests on federally managed lands to standreplacing harvest and wildfire, and we analyzed the amounts and spatial distribution of older forests by using the mapped data. We also performed statistical analysis on inventory plot information collected on Forest Service and Bureau of Land Management lands. These analyses provided statistically rigorous estimates of older forest acres bracketed by confidence intervals. We analyzed remeasured inventory plots to estimate net change in the amount of older forests on federally managed lands. We estimated the amount of older forest at the start of the Plan corresponding to three different older forest definitions based on average tree size, canopy layering, canopy closure, and life form. The results ranged from 7.87 million ac (± 1.96 million ac) of federally managed lands with average tree size at least 20 in (medium and large older forest), to 7.04 million ac (± 1.93 million ac) using a definition that recognizes variation in regional forest vegetation (older forest with size indexed to potential natural vegetation zone). We found 2.72 million ac (± 0.35 million ac) were in stands with average tree size 30 in and greater, with multistoried canopies (large, multistoried older forest). At least 1.7 million ac of existing “medium and large” older forest were in fire-adapted vegetation types characterized by high fire frequency and low severity in the Eastern Cascades and Klamath provinces. Up to 1 million additional older forest ac occurred in dry mixed-conifer types in the Western Cascades. Our data from remeasured inventory plots indicated that the annual net rate of increase of “medium and large” older forest was about 1.9 percent, outpacing losses from all sources. The extrapolated gain in older forest 20 in was between 1.25 million ac and 1.5 million ac in the first decade after the Plan. The gain came primarily from increases in the area of forest at the lower end of the diameter range for older forest. The net increase took into account the older forest removed by stand-replacing harvest, 0.2 percent of the total (about 16,900 ac on all federally managed lands), and the amount burned by stand-replacing wildfire, about 1.3 percent (about 102,500 ac on all federally managed lands). The area mapped as logged or burned had an error estimate of between 7 and 12 percent. The initial amount, distribution, and arrangement of older forest on federally managed land appears to have met or exceeded Northwest Forest Plan expectations. But the large amount of older forest susceptible to catastrophic wildfire may be a concern for managers. Losses to wildfire in the first decade were in line with assumptions for the Plan area, but rates of loss were highly variable among provinces, with the highest rates of loss occurring in the dry provinces. Loss of older forest to harvest was a fraction of the approximately 230,000 ac of older forest expected to have been harvested. Overall gain was about twice the 600,000 ac expected during the first decade of the Plan.

The terrestrial ecosystems of the North American continent represent a large reservoir of carbon and a potential sink within the global carbon cycle. The recent State of the Carbon Cycle Report [U.S. Climate Change Science Program (CCSP), 2007] identified the critical role these systems may play in mitigating effects of greenhouse gases emitted from fossil fuel combustion. However, there are currently large uncertainties in continental carbon models, and the scientific community's understanding of relevant carbon sources and sinks has been much less complete in Mexico than in Canada and the United States [Birdsey et al., 2007]. One reason for this disparity has been a lack of systematic field data from throughout Mexico. Recently, though, new field and satellite inventory information has become available through two Mexican resource agencies, the Comision Nacional Forestal (CONAFOR) and the Instituto Nacional de Estadstica y Geografa (INEGI). This information has the potential to support new types of analysis within Mexico and to significantly augment efforts to characterize carbon dynamics at the continental scale. Moreover, because many of the forest types and land use issues present in Mexico can be found throughout Latin America, Mexico's support of carbon monitoring may provide a relevant example as other counties within the region develop their own inventories.

Exogenous disturbances are critical agents of change in temperate forests capable of damaging trees and influencing forest structure, composition, demography, and ecosystem processes. Forest disturbances of intermediate magnitude and intensity receive relatively sparse attention, particularly at landscape scales, despite influencing most forests at least once per generation. Contextualizing the spatial extent and heterogeneity of such damage is of paramount importance to increasing our understanding of forested ecosystems. We investigated patterns of intermediate wind disturbance across a forested landscape in the northern Great Lakes, USA. A vegetation change tracker (VCT) algorithm was utilized for processing near-biennial Landsat data stacks (1984–2009) spanning forests sustaining damage from four recent windstorms. VCT predominantly maps stand-clearing disturbance and regrowth patterns, which were used to identify forest boundaries, young stands, and disturbance patterns across space and time. To map wind damage severity, we compared satellite-derived normalized difference vegetation index (NDVI) values calculated from pre- and post-storm Landsat imagery. A geographic information system (GIS) was used to derive wind damage predictor variables from VCT, digital terrain, soils/landform, land cover, and storm tracking data. Hierarchical and random forests regressions were applied to rank the relative importance of predictor variables in influencing wind damage. A conservative estimate of aggregate damage from the intermediate windstorms (extrapolated to ∼150,000 ha, ∼25,500 severe) rivaled individual large, infrequent disturbances in the region. Damage patterns were relatively congruent among storms and became more spatially heterogeneous with increasing disturbance intensity. Proximity to forest-nonforest edge, stand age, and soils/landform were consistently important damage predictors. The spatial extent and distribution of the first two damage predictors are extremely sensitive to anthropogenic modifications of forested landscapes, the most important disturbance agent in the northern Great Lakes. This provides circumstantial evidence suggesting anthropogenic activities are augmenting and/or diminishing the ecological effects of the natural wind disturbance regime. Natural disturbances of intermediate size and intensity are significant agents of change in this region, and likely in other regions, deserving more attention from ecologists and biogeographers.

North America's forests are thought to be a significant sink for atmospheric carbon. Currently, the rate of sequestration by forests on the continent has been estimated at 0.23 petagrams of carbon per year, though the uncertainty about this estimate is nearly 50%. This offsets about 13% of the fossil fuel emissions from the continent [Pacala et al., 2007]. However, the high level of uncertainty in this estimate and the scientific community's limited ability to predict the future direction of the forest carbon flux reflect a lack of detailed knowledge about the effects of forest disturbance and recovery across the continent. The North American Carbon Program (NACP), an interagency initiative to better understand the distribution, origin, and fate of North American sources and sinks of carbon, has highlighted forest disturbance as a critical factor constraining carbon dynamics [Wofsy and Harris, 2002]. National forest inventory programs in Canada, the United States, and Mexico provide important information, but they lack the needed spatial and temporal detail to support annual estimation of carbon fluxes across the continent. To help with this, the NACP recommends that scientists use detailed remote sensing of the land surface to characterize disturbance.

The temporal depth and spatial breadth of observations from platforms such as Landsat provide unique perspective on ecosystem dynamics, but the integration of these observations into formal decision support will rely upon improved uncertainty accounting. Monte Carlo (MC) simulations offer a practical, empirical method of accounting for potential map errors in broader ecosystem assessments. However, unless steps are taken across simulations to vary the probability density functions (PDFs) that control simulated map error, the large number of map units to which those PDFs are applied may cause convergence of mean simulated conditions and an artificial reduction of MC estimates of uncertainty. For MC simulation of errors in categorical maps, we introduce a technique we call “PDF weaving” which both: 1) allows variation of PDFs across simulations; and, 2) explicitly aligns the resulting range of simulated populations with estimates and uncertainties identified by traditional monitoring methods such as design-based inventories. This approach is based on solving systems of linear equations and inequalities for each simulation. Each system incorporates linear constraints related to the unchanging distribution of area among classes in the original map (akin to the fixed longitudinal “warp” on a loom) with variable linear constraints related to the class distribution to be simulated in any one iteration (analogous to the perpendicular, variable fibers of the “weft”). Additional constraints specify how many map units to treat as “correct” based on validation exercises at the map unit level. Solution of these systems provides PDFs which will simulate error at both the map unit level and the population level in a way that is consistent with validation exercises and available population-level estimates. We illustrated this approach in an assessment of the effects of wildfire and harvest on carbon storage over 20 years on a forested landscape in the western United States (US). This assessment utilized the Forest Carbon Management Framework (ForCaMF) approach, which is being implemented by the US National Forest System (NFS). Results showed that simulating map error through the use of dynamic PDFs can contribute significant, realistic uncertainty in a Monte Carlo analysis, but that impacts of fire and harvest on carbon storage may nevertheless be clearly identified and differentiated using remotely sensed maps of vegetation and disturbance.

Background Global forests capture and store significant amounts of CO2 through photosynthesis. When carbon is removed from forests through harvest, a portion of the harvested carbon is stored in wood products, often for many decades. The United States Forest Service (USFS) and other agencies are interested in accurately accounting for carbon flux associated with harvested wood products (HWP) to meet greenhouse gas monitoring commitments and climate change adaptation and mitigation objectives. This paper uses the Intergovernmental Panel on Climate Change (IPCC) production accounting approach and the California Forest Project Protocol (CFPP) to estimate HWP carbon storage from 1906 to 2010 for the USFS Northern Region, which includes forests in northern Idaho, Montana, South Dakota, and eastern Washington. Results Based on the IPCC approach, carbon stocks in the HWP pool were increasing at one million megagrams of carbon (MgC) per year in the mid 1960s, with peak cumulative storage of 28 million MgC occurring in 1995. Net positive flux into the HWP pool over this period is primarily attributable to high harvest levels in the mid twentieth century. Harvest levels declined after 1970, resulting in less carbon entering the HWP pool. Since 1995, emissions from HWP at solid waste disposal sites have exceeded additions from harvesting, resulting in a decline in the total amount of carbon stored in the HWP pool. The CFPP approach shows a similar trend, with 100-year average carbon storage for each annual Northern Region harvest peaking in 1969 at 937,900 MgC, and fluctuating between 84,000 and 150,000 MgC over the last decade. Conclusions The Northern Region HWP pool is now in a period of negative net annual stock change because the decay of products harvested between 1906 and 2010 exceeds additions of carbon to the HWP pool through harvest. However, total forest carbon includes both HWP and ecosystem carbon, which may have increased over the study period. Though our emphasis is on the Northern Region, we provide a framework by which the IPCC and CFPP methods can be applied broadly at sub-national scales to other regions, land management units, or firms.

Background Although significant amounts of carbon may be stored in harvested wood products, the extraction of that carbon from the forest generally entails combustion of fossil fuels. The transport of timber from the forest to primary milling facilities may in particular create emissions that reduce the net sequestration value of product carbon storage. However, attempts to quantify the effects of transport on the net effects of forest management typically use relatively sparse survey data to determine transportation emission factors. We developed an approach for systematically determining transport emissions using: 1) -remotely sensed maps to estimate the spatial distribution of harvests, and 2) - industry data to determine landscape-level harvest volumes as well as the location and processing totals of individual mills. These data support spatial network analysis that can produce estimates of fossil carbon released in timber transport. Results Transport-related emissions, evaluated as a fraction of transported wood carbon at 4 points in time on a landscape in western Montana (USA), rose from 0.5% in 1988 to 1.7% in 2004 as local mills closed and spatial patterns of harvest shifted due to decreased logging on federal lands. Conclusion The apparent sensitivity of transport emissions to harvest and infrastructure patterns suggests that timber haul is a dynamic component of forest carbon management that bears further study both across regions and over time. The monitoring approach used here, which draws only from widely available monitoring data, could readily be adapted to provide current and historical estimates of transport emissions in a consistent way across large areas.