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Context Recent increases in ecological disturbances driven by climate change and our expanding human footprint make it challenging for natural resource managers to keep apprised of current conditions and adjust management plans accordingly. To effectively conserve species in highly dynamic landscapes requires more timely habitat monitoring and a more responsive adaptive management cycle. Objectives We introduce a framework to automatically monitor and assess species habitats over a range of spatial and temporal scales. We then apply this framework by developing an automated habitat monitoring system for the Mexican spotted owl (MSO) in Arizona and New Mexico, USA, that will be linked to federal agency adaptive management plans. Methods We automated the process of monitoring and assessing trends in MSO habitat on an annual schedule using the Google Earth Engine cloud-based spatial analysis platform and dynamic data repository. We ran this system retrospectively on historical data to monitor MSO habitat from 1986 to 2020. Results The automated habitat monitoring system provided a 35-year MSO habitat time series with high accuracy. Widespread habitat gains and losses occurred every year, underscoring the need for continuous monitoring and the benefits of an automated workflow. Conclusions Automated habitat monitoring linked to adaptive management holds great promise in helping managers track the impacts of recent disturbances and adjust plans to meet goals even in increasingly dynamic landscapes. In a companion paper, Jones et al. (2023) demonstrate the utility of this approach by analyzing our MSO habitat time series to assess trends, drivers of change, and management implications.

Context Understanding habitat dynamics is essential for effective conservation as landscapes rapidly change. In a companion paper in this issue, Shirk et al. (2023) introduced an automated habitat monitoring system using Google Earth Engine and applied this framework to develop a dynamic model of Mexican spotted owl (Strix occidentalis lucida) habitat across the southwestern US from 1986 to 2020. Objectives We explored the application of this dynamic model of Mexican spotted owl habitat in the context of the species’ ecology. Methods We evaluated environmental correlates of Mexican spotted owl habitat, assessed potential spatial non-stationarity in habitat selection, estimated long-term trends in habitat by quantifying changes in habitat amount and quality between 1986 and 2020, and evaluated the extent to which habitat changes over the past 35 years have been driven by wildfire. Results Topography and climate appeared to outweigh reflectance-based (vegetation) metrics in describing Mexican spotted owl habitat and habitat selection was non-stationary across modeling sub-regions. Total habitat area for Mexican spotted owls declined by ~ 21% since 1986 (0.6% annually), but trends varied spatially and some even reversed over the past decade. Wildfire was responsible for between 8 and 35% of total habitat loss, depending on the sub-region considered. Conclusions The automated habitat monitoring system allowed trend estimation and accurate assessment of current habitat status for Mexican spotted owls; maps were accurate, spatially detailed, and current. The ability to continually produce accurate maps for large land areas for threatened species such as the Mexican spotted owl facilitates science-based land management on public lands in the southwestern US.

NASA's Global Ecosystem Dynamics Investigation (GEDI) mission offers data for temperate and pan-tropical estimates of aboveground forest biomass (AGB). The spaceborne, full-waveform LiDAR from GEDI provides sample footprints of canopy structure, expected to cover about 4% of the land area following two years of operation. Several options are available for estimating AGB at different geographical scales. Using GEDI sample data alone, gridded biomass predictions are based on hybrid inference which correctly propagates errors due to the modeling and accounts for sampling variability, but this method requires at least two GEDI tracks in the area of interest. However, there are significant gaps in GEDI coverage and in some areas of interest GEDI data may need to be combined with other wall-to-wall remotely sensed (RS) data, such as those from multispectral or SAR sensors. In these cases, we may employ hierarchical model-based (HMB) inference that correctly considers the additional model errors that result from relating GEDI data to the wall-to-wall data. Where predictions are possible from both hybrid and HMB inference the question arises which framework to choose, and under what circumstances? In this paper, we make progress towards answering these questions by comparing the performance of the two prediction frameworks under conditions relevant for the GEDI mission. Conventional model-based (MB) inference with wall-to-wall TanDEM-X data was applied as a baseline prediction framework, which does not involve GEDI data at all. An important feature of the study was the comparison of AGB predictors in terms of both standard deviation (SD: the square root of variance) and root mean square error (RMSE: the square root of mean square error – MSE). Since, in model-based inference, the true AGB in an area of interest is a random variable, comparisons of the performance of prediction frameworks should preferably be made in terms of their RMSEs. However, in practice only the SD can be estimated based on empirical survey data, and thus it is important also to study whether or not the difference between the two uncertainty measures is small or large under conditions relevant for the GEDI mission. Our main findings were that: (i) hybrid and HMB prediction typically resulted in smaller RMSEs than conventional MB prediction although the difference between the three frameworks in terms of SD often was small; (ii) in most cases the difference between hybrid and HMB inference was small in terms of both RMSE and SD; (iii) the RMSEs for all frameworks was substantially larger than the SDs in small study areas whereas the two uncertainty measures were similar in large study areas, and; (iv) spatial autocorrelation of model residual errors had a large effect on the RMSEs of AGB predictors, especially in small study areas. We conclude that hybrid inference is suitable in most GEDI applications for AGB assessment, due to its simplicity compared to HMB inference. However, where GEDI data are sparse HMB inference should be preferred.

In 2012, the US Forest Service promulgated new regulations for land-management planning that emphasize the importance of scientifically credible assessment and monitoring strategies for adaptive forest planning and the maintenance or restoration of ecological integrity. However, in an era of declining budgets, the implementation of robust assessment and monitoring strategies represents a significant challenge for fulfilling the intent of the new planning rule. In this article, we explore opportunities for using data and products produced by the USDA Forest Service’s Forest Inventory and Analysis (FIA) Program to support the implementation of the 2012 Planning Rule. FIA maintains a nationally consistent statistical sample of field plots that covers most national forests with hundreds of plots. We suggest that leveraging FIA data and products can generate efficiencies for assessment, planning, and monitoring requirements detailed in the 2012 Planning Rule, and help fulfill the adaptive intent of the new planning rule. However, strong national leadership and investment in regional-level analytical capacity, FIA liaisons, and decision-support tools are essential for systematically realizing the benefits of FIA data for forest planning across the National Forest System.

The United States (U.S.) federal government provides imagery obtained by federally funded Earth Observation satellites typically at no cost. For many years Landsat was an exception to this trend, until 2008 when the United States Geological Survey (USGS) made Landsat data accessible via the internet for free. Substantial increases in downloads of Landsat imagery ensued and led to a rapid expansion of science and operational applications, serving government, private sector, and civil society. The Landsat program hence provides an example to space agencies worldwide on the value of open access for Earth Observation data and has spurred the adaption of similar policies globally, including the European Copernicus Program. Here, we describe important aspects of the Landsat free and open data policy and highlight the importance and continued relevance of this policy.

Humid tropical forests play a dominant role in the functioning of Earth but are under increasing threat from changes in land use and climate. How forest vulnerability varies across space and time and what level of stress forests can tolerate before facing a tipping point are poorly understood. Here, we develop a tropical forest vulnerability index (TFVI) to detect and evaluate the vulnerability of global tropical forests to threats across space and time. We show that climate change together with land-use change have slowed the recovery rate of forest carbon cycling. Temporal autocorrelation, as an indicator of this slow recovery, increases substantially for above-ground biomass, gross primary production, and evapotranspiration when climate stress reaches a critical level. Forests in the Americas exhibit extensive vulnerability to these stressors, while in Africa, forests show relative resilience to climate, and in Asia reveal more vulnerability to land use and fragmentation. TFVI can systematically track the response of tropical forests to multiple stressors and provide early-warning signals for regions undergoing critical transitions.

Obtaining accurate and widespread measurements of the vertical structure of the Earth’s forests has been a long-sought goal for the ecological community. Such observations are critical for accurately assessing the existing biomass of forests, and how changes in this biomass caused by human activities or variations in climate may impact atmospheric CO2 concentrations. Additionally, the three-dimensional structure of forests is a key component of habitat quality and biodiversity at local to regional scales. The Global Ecosystem Dynamics Investigation (GEDI) was launched to the International Space Station in late 2018 to provide high-quality measurements of forest vertical structure in temperate and tropical forests between 51.6° N & S latitude. The GEDI instrument is a geodetic-class laser altimeter/waveform lidar comprised of 3 lasers that produce 8 transects of structural information. Over its two-year nominal lifetime GEDI is anticipated to provide over 10 billion waveforms at a footprint resolution of 25 ​m. These data will be used to derive a variety of footprint and gridded products, including canopy height, canopy foliar profiles, Leaf Area Index (LAI), sub-canopy topography and biomass. Additionally, data from GEDI are used to demonstrate the efficacy of its measurements for prognostic ecosystem modeling, habit and biodiversity studies, and for fusion using radar and other remote sensing instruments. GEDI science and technology are unique: no other space-based mission has been created that is specifically optimized for retrieving vegetation vertical structure. As such, GEDI promises to advance our understanding of the importance of canopy vertical variations within an ecological paradigm based on structure, composition and function.

Disturbance monitoring is an important application of the Landsat times series, both to monitor forest dynamics and to support wise forest management at a variety of spatial and temporal scales. In the last decade, there has been an acceleration in the development of approaches designed to put the Landsat archive to use towards these causes. Forest disturbance mapping has moved from using individual change-detection algorithms, which implement a single set of decision rules that may not apply well to a range of scenarios, to compiling ensembles of such algorithms. One approach that has greatly reduced disturbance detection error has been to combine individual algorithm outputs in Random Forest (RF) ensembles trained with disturbance reference data, a process called stacking (or secondary classification). Previous research has demonstrated more robust and sensitive detection of disturbance using stacking with both multialgorithm ensembles and multispectral ensembles (which make use of a single algorithm applied to multiple spectral bands). In this paper, we examined several additional dimensions of this problem, including: (1) type of algorithm (represented by processes using one image per year vs. all historical images); (2) spectral band choice (including both the basic Landsat reflectance bands and several popular indices based on those bands); (3) number of algorithm/spectral-band combinations needed; and (4) the value of including both algorithm and spectral band diversity in the ensembles. We found that ensemble performance substantially improved per number of model inputs if those inputs were drawn from a diversity of both algorithms and spectral bands. The best models included inputs from both algorithms, using different variants of shortwave-infrared (SWIR) and near-infrared (NIR) reflectance. Further disturbance detection improvement may depend upon the development of algorithms which either interrogate SWIR and NIR in new ways or better highlight disturbance signals in the visible wavelengths.

Accurate characterization of Carbon (C) consequences of forest disturbances and management is critical for informed climate mitigation and adaptation strategies. While research into generalized properties of the forest C cycle informs policy and provides abstract guidance to managers, most management occurs at local scales and relies upon monitoring systems that can consistently provide C cycle assessments that explicitly apply to a defined time and place. We used an inventory-based forest monitoring and simulation tool to quantify C storage effects of actual fires, timber harvests, and forest regeneration conditions in the Greater Yellowstone Ecosystem (GYE). Results show that (1) the 1988 fires had a larger impact on GYE's C storage than harvesting during 1985-2011; (2) continuation of relatively high harvest rates of the region's National Forest land, which declined after 1990, would have shifted the disturbance agent primary importance on those lands from fire to harvest; and (3) accounting for local heterogeneity of post-disturbance regeneration patterns translates into large regional effects on total C storage. Large fires in 1988 released about 8.3 ± 0.3 Mg/ha of C across Yellowstone National Park (YNP, including both disturbed and undisturbed area), compared with total C storage reductions due to harvest of about 2.3 ± 0.3 Mg/ha and 2.6 ± 0.2 Mg/ha in adjacent Caribou-Targhee and Gallatin National Forests, respectively, from 1985-2011. If the high harvest rates observed in 1985-1989 had been maintained through 2011 in GYE National Forests, the C storage effect of harvesting would have quintupled to 10.5 ± 1.0 Mg/ha, exceeding the immediate losses associated with YNP's historic fire but not the longer-term net loss of carbon (16.9 ± 0.8 Mg/ha). Following stand-replacing disturbance such as the 1988 fires, the actual regeneration rate was slower than the default regional average rate assumed by empirically calibrated forest growth models. If regeneration following the 1988 fire had reached regionally average rates, either through different natural circumstances or through more active management, YNP would have had approximately 4.1 Mg/ha more forest carbon by year 2020. This study highlights the relative effects of fire disturbances and management activities on regional C storage, and demonstrates a forest carbon monitoring system that can be both applied consistently across the US and tailored to questions of specific local management interest.

Monitoring and classifying forest disturbance using Landsat time series has improved greatly over the past decade, with many new algorithms taking advantage of the high-quality, cost free data in the archive. Much of the innovation has been focused on use of sophisticated workflows that consist of a logical sequence of processes and rules, multiple statistical functions, and parameter sets that must be calibrated to accurately classify disturbance. For many algorithms, calibration has been local to areas of interest and the algorithm's classification performance has been good under those circumstances. When applied elsewhere, however, algorithm performance has suffered. An alternative strategy for calibration may be to use the locally tested parameter values in conjunction with a statistical approach (e.g., Random Forests; RF) to align algorithm classification with a reference disturbance dataset, a process we call secondary classification. We tested that strategy here using RF with LandTrendr, an algorithm that runs on one spectral band or index. Disturbance detection using secondary classification was spectral band- or index-dependent, with each spectral dimension providing some unique detections and different error rates. Using secondary classification, we tested whether an integrated multispectral LandTrendr ensemble, with various combinations of the six basic Landsat reflectance bands and seven common spectral indices, improves algorithm performance. Results indicated a substantial reduction in errors relative to secondary classification based on single bands/indices, revealing the importance of a multispectral approach to forest disturbance detection. To explain the importance of specific bands and spectral indices in the multispectral ensemble, we developed a disturbance signal-to-noise metric that clearly highlighted the value of shortwave-infrared reflectance, especially when paired with near-infrared reflectance.

The ever-increasing volume and accessibility of remote sensing data has spawned many alternative approaches for mapping important environmental features and processes. For example, there are several viable but highly varied strategies for using time series of Landsat imagery to detect changes in forest cover. Performance among algorithms varies across complex natural systems, and it is reasonable to ask if aggregating the strengths of an ensemble of classifiers might result in increased overall accuracy. Relatively simple rules have been used in the past to aggregate classifications among remotely sensed maps (e.g. using majority predictions), and in other fields, empirical models have been used to create situationally specific algorithm weights. The latter process, called “stacked generalization” (or “stacking”), typically uses a parametric model for the fusion of algorithm outputs. We tested the performance of several leading forest disturbance detection algorithms against ensembles of the outputs of those same algorithms based upon stacking using both parametric and Random Forests-based fusion rules. Stacking using a Random Forests model cut omission and commission error rates in half in many cases in relation to individual change detection algorithms, and cut error rates by one quarter compared to more conventional parametric stacking. Stacking also offers two auxiliary benefits: alignment of outputs to the precise definitions built into a particular set of empirical calibration data; and, outputs which may be adjusted such that map class totals match independent estimates of change in each year. In general, ensemble predictions improve when new inputs are added that are both informative and uncorrelated with existing ensemble components. As increased use of cloud-based computing makes ensemble mapping methods more accessible, the most useful new algorithms may be those that specialize in providing spectral, temporal, or thematic information not already available through members of existing ensembles.

Management of forest carbon stocks on public lands is critical to maintaining or enhancing carbon dioxide removal from the atmosphere. Acknowledging this, an array of federal regulations and policies have emerged that requires US National Forests to report baseline carbon stocks and changes due to disturbance and management and assess how management activities and forest plans affect carbon stocks. To address these requirements with the best-available science, we compiled empirical and remotely sensed data covering the National Forests (one fifth of the area of US forest land) and analyzed this information using a carbon modeling framework. We demonstrate how integration of various data and models provides a comprehensive evaluation of key drivers of observed carbon trends, for individual National Forests. The models in this framework complement each other with different strengths: the Carbon Calculation Tool uses inventory data to report baseline carbon stocks; the Forest Carbon Management Framework integrates inventory data, disturbance histories, and growth and yield trajectories to report relative effects of disturbances on carbon stocks; and the Integrated Terrestrial Ecosystem Carbon Model incorporates disturbance, climate, and atmospheric data to determine their relative impacts on forest carbon accumulation and loss. We report results for several National Forests across the USA and compare their carbon dynamics. Results show that recent disturbances are causing some forests to transition from carbon sinks to sources, particularly in the West. Meanwhile, elevated atmospheric carbon dioxide and nitrogen deposition are consistently increasing carbon stocks, partially offsetting declines due to disturbances and aging. Climate variability introduces concomitant interannual variability in net carbon uptake or release. Targeting forest disturbance and post-disturbance regrowth is critical to management objectives that involve maintaining or enhancing future carbon sequestration.

Population growth rates in Sub-Saharan East Africa are among the highest in the world, creating increasing pressure for land cover conversion. To date, however, there has been no comprehensive assessment of regional land cover change, and most long-term trends have not yet been quantified. Using a designed sample of satellite-based observations of historical land cover change, we estimate the areas and trends in nine land cover classes from 1998 to 2017 in Ethiopia, Kenya, Uganda, Malawi, Rwanda, Tanzania, and Zambia. Our analysis found an 18,154,000 (±1,580,000) ha, or 34.8%, increase in the area of cropland in East Africa. Conversion occurred primarily from Open Grasslands, Wooded Grasslands, and Open Forests, causing a large-scale reduction in woody vegetation classes. We observed far more conversion (by approximately 20 million hectares) of woody classes to less-woody classes than succession in the direction of increasing trees and shrubs. Spatial patterns within our sample highlight regional land cover conversion hotspots, such as the Central Zambezian Miombo Woodlands, as potential areas of concern related to the conservation of natural ecosystems. Our findings reflect a rapidly growing population that is moving into new areas, with a 43.5% increase in the area of Settlements over the three-decade period. Our results show the areas and ecoregions most impacted by three decades of human development, both spatially and statistically.

Globally, forests are a crucial natural resource, and their sound management is critical for human and ecosystem health and well-being. Efforts to manage forests depend upon reliable data on the status of and trends in forest resources. When these data come from well-designed natural resource monitoring (NRM) systems, decision makers can make science-informed decisions. National forest inventories (NFIs) are a cornerstone of NRM systems, but require capacity and skills to implement. Efficiencies can be gained by incorporating auxiliary information derived from remote sensing (RS) into ground-based forest inventories. However, it can be difficult for countries embarking on NFI development to choose among the various RS integration options, and to develop a harmonized vision of how NFI and RS data can work together to meet monitoring needs. The NFI of the United States, which has been conducted by the USDA Forest Service’s (USFS) Forest Inventory and Analysis (FIA) program for nearly a century, uses RS technology extensively. Here we review the history of the use of RS in FIA, beginning with general background on NFI, FIA, and sampling statistics, followed by a description of the evolution of RS technology usage, beginning with paper aerial photography and ending with present day applications and future directions. The goal of this review is to offer FIA’s experience with NFI-RS integration as a case study for other countries wishing to improve the efficiency of their NFI programs.

Throughout the last three decades, north central Georgia has experienced significant loss in forest land and tree cover. This study revealed the temporal patterns and thematic transitions associated with this loss by augmenting traditional forest inventory data with remotely sensed observations. In the US, there is a network of field plots measured consistently through time from the USDA Forest Service&rsquo, s Forest Inventory and Analysis (FIA) Program, serial photo-based observations collected through image-based change estimation (ICE) methodology, and historical Landsat-based observations collected through TimeSync. The objective here was to evaluate how these three data sources could be used to best estimate land use and land cover (LULC) change. Using data collected in north central Georgia, we compared agreement between the three data sets, assessed the ability of each to yield adequately precise and temporally coherent estimates of land class status as well as detect net and transitional change, and we evaluated the effectiveness of using remotely sensed data in an auxiliary capacity to improve detection of statistically significant changes. With the exception of land cover from FIA plots, agreement between paired data sets for land use and cover was nearly 85%, and estimates of land class proportion were not significantly different for overlapping time intervals. Only the long time series of TimeSync data revealed significant change when conducting analyses over five-year intervals and aggregated land categories. Using ICE and TimeSync data through a two-phase estimator improved precision in estimates but did not achieve temporal coherence. We also show analytically that using auxiliary remotely sensed data for post-stratification for binary responses must be based on maps that are extremely accurate in order to see gains in precision. We conclude that, in order to report LULC trends in north central Georgia with adequate precision and temporal coherence, we need data collected on all the FIA plots each year over a long time series and broadly collapsed LULC classes.

This report assesses how carbon stocks at regional scales and in individual national forests are affected by factors such as timber harvesting, natural disturbances, climate variability, increasing atmospheric carbon dioxide concentrations, and nitrogen deposition. Previous baseline assessments of carbon stocks (https://www.fs.fed.us/managing-land/sc/carbon) evaluated observed trends based on forest inventory data but were limited in ability to reveal detailed causes of these trends. The expanded assessments reported here are based on an extensive disturbance and climate history for each national forest, and two forest carbon models, to estimate the relative impacts of disturbance (e.g., fires, harvests, insect outbreaks, disease) and nondisturbance factors (climate, carbon dioxide concentration, nitrogen deposition). Results are summarized for each region of the National Forest System in the main document. A set of regional appendices to this report provides more detailed information about individual national forests within each region. Results are highly variable across the United States. Generally, carbon stocks are increasing in forests of the eastern United States as these forests continue to recover and grow older after higher historical harvesting rates and periods of nonforest land use. In contrast, carbon stocks in forests of the western United States may be either increasing or decreasing, depending on recent effects of natural disturbances and climate change. The information supports national forest units in assessing carbon stocks, quantifying carbon outcomes of broad forest management strategies and planning, and meeting carbon assessment requirements of the 2012 Planning Rule and directives. Results of these expanded assessments will provide context for project-level decisions, separated from the effects of factors that are beyond land managers’ control.

Research Highlights: Estimates using measurements from a sample of approximately 132,000 field plots imply that while the species composition of US forests varies substantially across different age groups, the specific gravity of wood in those forests does not. This suggests that models using increasingly accurate spaceborne measurements of tree size to model forest biomass do not need to consider stand age as a covariate, greatly reducing model complexity and calibration data requirements. Background and Objectives: Upcoming lidar and radar platforms will give us unprecedented information about how big the trees around the world are. To estimate biomass from these measurements, one must know if tall trees in young stands have the same biomass density as trees of equal size in older stands. Conventional succession theory suggests that fast-growing pioneers often have lower wood (and biomass) density than the species that eventually dominate older stands. Materials and Methods: We used a nationally consistent database of field measurements to analyze patterns of both wood specific gravity (WSG) across age groups in the United States and changes of species composition that would explain any shifts in WSG. Results: Shifts in species composition were observed across 12 different ecological divisions within the US, reflecting both successional processes and management history impacts. However, steady increases in WSG with age were not observed, and WSG differences were much larger across ecosystems than across within-ecosystem age groups. Conclusions: With no strong evidence that age is important in specifying how much biomass to ascribe to trees of a particular size, field data collection can focus on acquiring reference data in poorly sampled ecosystems instead of expanding existing samples to include a range of ages for each level of canopy height.

The U.S. Geological Survey Land Change Monitoring, Assessment and Projection (USGS LCMAP) initiative is working toward a comprehensive capability to characterize land cover and land cover change using dense Landsat time series data. A suite of products including annual land cover maps and annual land cover change maps will be produced using the Landsat 4-8 data record. LCMAP products will initially be created for the conterminous United States (CONUS) and then extended to include Alaska and Hawaii. A critical component of LCMAP is the collection of reference data using the TimeSync tool, a web-based interface for manually interpreting and recording land cover from Landsat data supplemented with fine resolution imagery and other ancillary data. These reference data will be used for area estimation and validation of the LCMAP annual land cover products. Nearly 12,000 LCMAP reference sample pixels have been interpreted and a simple random subsample of these pixels has been interpreted independently by a second analyst (hereafter referred to as “duplicate interpretations”). The annual land cover reference class labels for the 1984–2016 monitoring period obtained from these duplicate interpretations are used to address the following questions: 1) How consistent are the reference class labels among interpreters overall and per class? 2) Does consistency vary by geographic region? 3) Does consistency vary as interpreters gain experience over time? 4) Does interpreter consistency change with improving availability and quality of imagery from 1984 to 2016? Overall agreement between interpreters was 88%. Class-specific agreement ranged from 46% for Disturbed to 94% for Water, with more prevalent classes (Tree Cover, Grass/Shrub and Cropland) generally having greater agreement than rare classes (Developed, Barren and Wetland). Agreement between interpreters remained approximately the same over the 12-month period during which these interpretations were completed. Increasing availability of Landsat and Google Earth fine resolution data over the 1984 to 2016 monitoring period coincided with increased interpreter consistency for the post-2000 data record. The reference data interpretation and quality assurance protocols implemented for LCMAP demonstrate the technical and practical feasibility of using the Landsat archive and intensive human interpretation to produce national, annual reference land cover data over a 30-year period. Protocols to estimate and enhance interpreter consistency are critical elements to document and ensure the quality of these reference data.

The water balance in a watershed can be disrupted by forest disturbances such as harvests and fires. Techniques to accurately and efficiently map forest cover changes due to disturbance are evolving quickly, and it is of interest to ask how useful maps of different types of disturbances over time can be in the prediction of water yield. We assessed the benefits of using land cover maps produced at annual vs. five-year intervals in the prediction of monthly streamflows across 10 watersheds contained entirely within the US National Forest System. We found that annually updating land cover maps with forest disturbance data significantly improved water yield predictions using the Soil and Water Assessment Tool (SWAT, p &lt, 0.01 improvement for both the Nash&ndash, Sutcliffe efficiency measure and the ratio of the root mean square error to the standard deviation of the measured data). Improvement related to using annually updated land cover maps was directly related to the amount of disturbance observed in a watershed. Our results lay a foundation to apply new high-resolution disturbance datasets in the field of hydrologic modeling to monitor ungauged watersheds and to explore potential water yield changes in watersheds if climate conditions or management practices were to change forest disturbance processes.

Sustaining forest resources requires a better understanding of forest ecosystem processes, and how management decisions and climate change may affect these processes in the future. While plot and inventory data provide our most detailed information on forest carbon, energy, and water cycling, applying this understanding to broader spatial and temporal domains requires scaling approaches. Remote sensing provides a powerful resource for ‘‘upscaling’’ process understanding to regional and continental domains. The increased range of available remote sensing modalities, including interferometric radar, lidar, and hyperspectral imagery, allows the retrieval of a broad range of forest attributes. This paper reviews the application of remote sensing for upscaling forest attributes from the plot scale to regional domains, with particular emphasis on how remote sensing products can support parameterization and validation of ecosystem process models. We focus on four key ecological attributes of forests: composition, structure, productivity and evapotranspiration, and disturbance dynamics. For each attribute, we discuss relevant remote sensing technologies, provide examples of their application, and critically evaluate both strengths and challenges associated with their use.

Disturbance is a key driver of carbon (C) dynamics in forests. Insect epidemics, wildfires, and timber harvest have greatly affected North American C budgets in the last century. Research is needed to understand how forest C dynamics (source duration and recovery time) following disturbance vary as a function of disturbance type, severity, forest type, and initial C stocks. We used the Forest Vegetation Simulator (FVS) to simulate total C stocks (excluding soil) for 100 years following three types of disturbance (fire, harvest, and insects) with four levels of severity. We initiated the model using empirical data from a large representative sample of forest conditions on the national forest ownership in the Rocky Mountain region (Forest Inventory and Analysis data). Unlike analyses based on stand age, an ambiguous quantity with respect to disturbance history, our approach enables explicit consideration of disturbance type and severity, as well as pre-disturbance forest C. On average, stands became a C sink after fire in 5, 6, 14, and 23 years for low to high-severity fire. Pre-fire C stocks were reached 25–55 years later. Following bark beetle epidemics, on average stands continued to be a C source for 10 years longer than fire and up to 40 years longer in some cases, but pre-disturbance C stocks were reached in a similar amount of time. C stocks following harvest showed the largest initial decline, but on average stands became a sink sooner at 1, 5, 15, and 12 years post-harvest for low to high-severity harvests. Differences in C dynamics based on disturbance type and severity, initial conditions, and forest type demonstrate the importance of considering this variability when modeling forest C dynamics. The regionally averaged models of C response quantified in this study can be combined with remotely sensed data on disturbance type and severity and used with C accounting approaches that rely on growth and yield or state and transition models.

article i nfo With earth's surface temperature and human population both on the rise a new emphasis has been placed on monitoring changes to forested ecosystems the world over. In the United States the U.S. Forest Service Forest Inventoryand Analysis(FIA) programmonitorstheforestedlandbase with field data collectedovera permanent network of sample plots. Although these plots are visited repeatedly through time there are large temporal gaps (e.g. 5-10 years) between remeasurements such that many forest canopy disturbances go undetected. In this paper we demonstrate how Landsat time series (LTS) can help improve FIA's capacity to estimate disturbance by 1.) incorporating a new, downward looking response variable which is more sensitive to picking up change and 2.) providing historical disturbance maps which can reduce the variance of design-based estimates via post-stratification. To develop the LTS response variable a trained analyst was used to manually interpret 449 forestedFIAplotslocatedintheUintaMountainsofnorthernUtah,USA.Thisinvolvedrecordingcauseandtiming of disturbances based on evidence gathered from a 26-year annual stack of Landsat images and an 18-year, periodically spaced set of high resolution (~1 m) aerial photographs (e.g. National Aerial Image Program, NAIP and Google Earth). In general, the Landsat data captured major disturbances (e.g. harvests, fires) while the air photos allowed more detailed estimates of the number of trees impacted by recent insect outbreaks. Comparing the LTS and FIA field observations, we found that overall agreement was 73%, although when only disturbed plots were considered agreement dropped to 40%. Using the non-parametric Mann-Whitney test, we compared distributions of live and disturbed tree size (height and DBH) and found that when LTS and FIA both found non- stand clearing disturbance the median disturbed tree size was significantly larger than undisturbed trees, whereas no significant difference was found on plots where only FIA detected disturbance. This suggests that LTS interpretation and FIA field crews both detect upper canopy disturbances while FIA crews alone add disturbances occurring at or below canopy level. The analysis also showed that plots with only LTS disturbance had a significantly greater median number of years since last FIA measurement (6 years) than plots with both FIA and LTS disturbances (2.5 years), indicating that LTS improved detection on plots which had not been field sampled for several years. Next, to gauge the impact of incorporating LTS disturbances into the FIA estimation process we calculated design-based estimates of disturbance (for the period 1995-2011) using three response populations 1.) LTS observations, 2.) FIA field observations, and 3.) Combination of FIA and LTS observations. The results showed that combining the FIA and LTS observations led to the largest and most precise (i.e. smallest percent standard error) estimates of disturbance. In fact, the estimate based on the combined observations (486,458 ha, +/−47,101) was approximately 65% more than the estimate derived solely with FIA data (294,295 ha, +/−44,242). Lastly, a Landsat forest disturbance map was developed and tested for its ability to post-stratify the design-based estimates. Based on relative efficiency (RE), we found that stratification mostly im- proved the estimates derived with the LTS response data. Aside from insects (RE = 1.26), the estimates of area af- fected by individual agents saw minimal gain, whereas the LTS and combined FIA + LTS estimates of total

When characterizing the processes that shape ecosystems, ecologists increasingly use the unique perspective offered by repeat observations of remotely sensed imagery. However, the concept of change embodied in much of the traditional remote-sensing literature was primarily limited to capturing large or extreme changes occurring in natural systems, omitting many more subtle processes of interest to ecologists. Recent technical advances have led to a fundamental shift toward an ecological view of change. Although this conceptual shift began with coarser-scale global imagery, it has now reached users of Landsat imagery, since these datasets have temporal and spatial characteristics appropriate to many ecological questions. We argue that this ecologically relevant perspective of change allows the novel characterization of important dynamic processes, including disturbances, longterm trends, cyclical functions, and feedbacks, and that these improvements are already facilitating our understanding of critical driving forces, such as climate change, ecological interactions, and economic pressures.

The critical role of forests in the global carbon cycle is well known, but significant uncertainties remain about the specific role of disturbance, in part because of the challenge of incorporating spatial and temporal detail in the characterization of disturbance processes. In this study, we link forest inventory data to remote sensing data to derive estimates of pre- and post-disturbance biomass, and then use near-annual remote sensing observations of forest disturbance to characterize biomass loss associated with disturbance across the conterminous U.S. between 1986 and 2004. Nationally, year-to-year variability in the amount of live aboveground carbon lost as a result of disturbance ranged from a low of 61 Tg C (±16) in 1991 to a high of 84 Tg C (±33) in 2003. Eastern and western forest strata were relatively balanced in terms of their proportional contribution to national-level trends, despite eastern forests having more than twice the area of western forests. In the eastern forest stratum, annual biomass loss tracked closely with the area of disturbance, whereas in the western forest stratum, annual biomass loss showed more year-to-year variability that did not directly correspond to the area of disturbance, suggesting that the biomass density of forests affected by disturbance in the west was more spatially and temporally variable. Eastern and western forest strata exhibited somewhat opposing trends in biomass loss, potentially corresponding to the implementation of the Northwest Forest Plan in the mid 1990s that resulted in a shift of timber harvesting from public lands in the northwest to private lands in the south. Overall, these observations document modest increases in disturbance rates and associated carbon consequences over the 18-year period. These changes are likely not significant enough to weaken a growing forest carbon sink in the conterminous U.S. based largely on increased forest growth rates and biomass densities.

article i nfo In Canadian boreal forests, wildfire is the predominant agent of natural disturbance often with millions of hectares burning annually. In addition to fire, nearly one quarter of Canada's boreal forest is also managed for industrial wood production. Post-fire logging (or salvage harvesting) is increasingly used to minimize economic losses from fire, notwithstanding that the ecological impacts of successive disturbance events remain poorly understood. Improved monitoring and management of post-fire environments will require new information regarding the location and timing of past operations. In this paper we present and evaluate a data integration approach for detecting spatial and temporal trends in post-fire logging. Here we utilize a series of maps relating timing and extent of burned area (from the Canadian Large Fire Database) and year of harvest (from Landsat change detection) to identify occurrences of post-fire logging between 1987 and 2008 for a portion of boreal forest located in central Saskatchewan, Canada. Using a design-based, stratified random sampling framework we found that 68% (95% confidence interval (CI) (53 to 84%)) of the detected post-fire logging was correctly classified, such that both fire and clearcutting disturbances were positively verified by the reference data. The majority of map error resulted from spectral confusion between harvested areas and rock outcroppings exposed by fire and from mislabeling harvested unburned islands as post-fire logging. To add further confidence to our classification accuracy, we also found that mapped post-fire logging displayed similar temporal trends over a ten year period as salvage volume reported for a forest management area partially contained within the study area. Based upon the significant relationship between these esti- mates (r=0.97, pb0.001) and the good degree of observed map accuracy, we believe that the presented approach offers a viable and flexible option for characterizing the spatial and temporal dynamics of post- fire logging in boreal forests. Maps which reliably identify areas of post-fire logging stand to improve our capacity to manage and model the ecological impacts associated with multiple disturbance events.

Accurate landscape-scale maps of forests and associated disturbances are critical to augment studies on biodiversity, ecosystem services, and the carbon cycle, especially in terms of understanding how the spatial and temporal complexities of damage sustained from disturbances influence forest structure and function. Vegetation change tracker (VCT) is a highly automated algorithm that exploits the spectral-temporal properties of summer Landsat time series stacks (LTSSs) to generate spatially explicit maps of forest and recent forest disturbances. VCT performs well in contiguous forest landscapes with closed or nearly closed canopies, but often incorrectly classifies large patches of land as forest or forest disturbance in the complex and spatially heterogeneous environments that typify fragmented forest landscapes. We introduce an improved version of VCT (dubbed VCTw) that incorporates a nonforest mask derived from snow-covered winter Landsat time series stacks ( LTSSw) and compare it with VCT across nearly 25 million ha of land in the Lake Superior (Canada, USA) and Lake Michigan (USA) drainage basins. Accuracy assessments relying on 87 primary sampling units (PSUs) and 2640 secondary sampling units (SSUs) indicated that VCT performed with an overall accuracy of 86.3%. For persisting forest, the commission error was 14.7% and the omission error was 4.3%. Commission and omission errors for the two forest disturbance classes fluctuated around 50%. VCTw produced a statistically significant increase in overall accuracy to 91.2% and denoted about 1.115 million ha less forest (- .371 million ha disturbed and -0.744 million ha persisting). For persisting forest, the commission error decreased to 9.3% and the omission error was relatively unchanged at 5.0%. Commission errors decreased considerably to near 22% and omission errors remained near 50% in both forest disturbance classes. Dividing the assessments into three geographic strata demonstrated that the most dramatic improvement occurred across the southern half of the Lake Michigan basin, which contains a highly fragmented agricultural landscape and relatively sparse deciduous forest, although substantial improvements occurred in other geographic strata containing little agricultural land, abundant wetlands, and extensive coniferous forest. Unlike VCT, VCTw also generally corresponded well with field-based estimates of forest cover in each stratum. Snow-covered winter imagery appears to be a valuable resource for improving automated disturbance mapping accuracy. About 34% of the world's forests receive sufficient snowfall to cover the ground and are potentially suitable for VCTw; other season-based techniques may be worth pursuing for the remaining 66%.

Understanding the potential of forest ecosystems as global carbon sinks requires a thorough knowledge of forest carbon dynamics, including both sequestration and fluxes among multiple pools. The accurate quantification of biomass is important to better understand forest productivity and carbon cycling dynamics. Stand-based inventories (SBIs) are widely used for quantifying forest characteristics and for estimating biomass, but information may quickly become outdated in dynamic forest environments. Satellite remote sensing may provide a supplement or substitute. We tested the accuracy of aboveground biomass estimates modeled from a combination of Landsat Thematic Mapper (TM) imagery and topographic data, as well as SBI-derived variables in a Picea abies forest in the Western Carpathian Mountains. We employed Random Forests for non-parametric, regression tree-based modeling. Results indicated a difference in the importance of SBI-based and remote sensing-based predictors when estimating aboveground biomass. The most accurate models for biomass prediction ranged from a correlation coefficient of 0.52 for the TM- and topography-based model, to 0.98 for the inventory-based model. While Landsat-based biomass estimates were measurably less accurate than those derived from SBI, adding tree height or stand-volume as a field-based predictor to TM and topography-based models increased performance to 0.36 and 0.86, respectively. Our results illustrate the potential of spectral data to reveal spatial details in stand structure and ecological complexity.

Information regarding the extent, timing and magnitude of forest disturbance are key inputs required for accurate estimation of the terrestrial carbon balance. Equally important for studying carbon dynamics is the ability to distinguish the cause or type of forest disturbance occurring on the landscape. Wildfire and timber harvesting are common disturbances occurring in boreal forests, with each having differing carbon consequences (i.e., biomass removed, recovery rates). Development of methods to not only map, but distinguish these types of disturbance with satellite data will depend upon an improved understanding of their distinctive spectral properties. In this study, we mapped wildfires and clearcut harvests occurring in a Landsat time series (LTS) acquired in the boreal plains of Saskatchewan, Canada. This highly accurate reference map (kappa = 0.91) depicting the year and cause of historical disturbances was used to determine the spectral and temporal properties needed to accurately classify fire and clearcut disturbances. The results showed that spectral data from the short-wave infrared (SWIR; e.g., Landsat band 5) portion of the electromagnetic spectrum was most effective at separating fires and clearcut harvests possibly due to differences in structure, shadowing, and amounts of exposed soil left behind by the two disturbance types. Although SWIR data acquired 1 year after disturbance enabled the most accurate discrimination of fires and clearcut harvests, good separation (e.g., kappa ≥ 0.80) could still be achieved with Landsat band 5 and other SWIR-based indices 3 to 4 years after disturbance. Conversely, minimal disturbance responses in near infrared-based indices associated with green leaf area (e.g., NDVI) led to unreliably low classification accuracies regardless of time since disturbance. In addition to exploring the spectral and temporal manifestation of forest disturbance types, we also demonstrate how Landsat change maps which attribute cause of disturbance can be used to help elucidate the social, ecological and carbon consequences associated with wildfire and clearcut harvesting in Canadian boreal forests.

The LandTrendr (LT) algorithm has been used widely for analysis of change in Landsat spectral time series data, but requires significant pre-processing, data management, and computational resources, and is only accessible to the community in a proprietary programming language (IDL). Here, we introduce LT for the Google Earth Engine (GEE) platform. The GEE platform simplifies pre-processing steps, allowing focus on the translation of the core temporal segmentation algorithm. Temporal segmentation involved a series of repeated random access calls to each pixel’s time series, resulting in a set of breakpoints (“vertices”) that bound straight-line segments. The translation of the algorithm into GEE included both transliteration and code analysis, resulting in improvement and logic error fixes. At six study areas representing diverse land cover types across the U.S., we conducted a direct comparison of the new LT-GEE code against the heritage code (LT-IDL). The algorithms agreed in most cases, and where disagreements occurred, they were largely attributable to logic error fixes in the code translation process. The practical impact of these changes is minimal, as shown by an example of forest disturbance mapping. We conclude that the LT-GEE algorithm represents a faithful translation of the LT code into a platform easily accessible by the broader user community.

Spatially and temporally explicit knowledge of biomass dynamics at broad scales is critical to understanding how forest disturbance and regrowth processes influence carbon dynamics. We modeled live, aboveground tree biomass using Forest Inventory and Analysis (FIA) field data and applied the models to 20+ year time-series of Landsat satellite imagery to derive trajectories of aboveground forest biomass for study locations in Arizona and Minnesota. We compared three statistical techniques (Reduced Major Axis regression, Gradient Nearest Neighbor imputation, and Random Forests regression trees) for modeling biomass to better understand how the choice of model type affected predictions of biomass dynamics. Models from each technique were applied across the 20+ year Landsat time-series to derive biomass trajectories, to which a curve-fitting algorithm was applied to leverage the temporal information contained within the time-series itself and to minimize error associated with exogenous effects such as biomass measurements, phenology, sun angle, and other sources. The effect of curve-fitting was an improvement in predictions of biomass change when validated against observed biomass change from repeat FIA inventories. Maps of biomass dynamics were integrated with maps depicting the location and timing of forest disturbance and regrowth to assess the biomass consequences of these processes over large areas and long time frames. The application of these techniques to a large sample of Landsat scenes across North America will facilitate spatial and temporal estimation of biomass dynamics associated with forest disturbance and regrowth, and aid in national-level estimates of biomass change in support of the North American Carbon Program.

The development of conservation plans for Northern Spotted Owls (NSO) (Strix occidentalis caurina) in disturbance-prone landscapes requires evaluation of multiple threats and careful consideration of the consequences of management actions intended to reduce risk. Hanson et al. (2009) used downwardly revised estimates of recent old-forest losses to high-severity wildfire to argue that the recent NSO recovery plan (USDI 2008) overestimates fire risk to the NSO in dry, fire-prone forests. We believe their analysis is erroneous and deficient and does not support their conclusions. Furthermore, they show a bias against active management by ignoring recent science and risk factors in dry forests that do not support their opinions.

Effects of a catastrophic blowdown event in northern Minnesota, USA were assessed using field inventory data, aerial sketch maps and satellite image data processed through the North American Forest Dynamics programme. Estimates were produced for forest area and net volume per unit area of live trees pre- and post-disturbance, and for changes in volume per unit area and total volume resulting from disturbance. Satellite image-based estimates of blowdown area were similar to estimates derived from inventory plots and aerial sketch maps. Overall accuracy of the image-based damage classification was over 90%. Compared to field inventory estimates, image-based estimates of post-blowdown mean volume per unit area were similar, but estimates of total volume loss were substantially larger, although inaccessibility of the most severely damaged inventory plots may have depressed the inventory-based estimate. This represents the first application of state model differencing to storm damage assessment. The image-based procedure can be applied to historical archives of satellite imagery and does not require pre-disturbance field inventory data.

Interest in preserving older forests at the landscape level has increased in many regions, including the Pacific Northwest of the United States. The Northwest Forest Plan (NWFP) of 1994 initiated a significant reduction in the harvesting of older forests on federal land. We used historical satellite imagery to assess the effect of this reduction in relation to: past harvest rates, management of non-federal forests, and the growing role of fire. Harvest rates in non-federal large-diameter forests (LDF) either decreased or remained stable at relatively high rates following the NWFP, meaning that harvest reductions on federal forests, which cover half of the region, resulted in a significant regional drop in the loss of LDF to harvest. However, increased losses of LDF to fire outweighed reductions in LDF harvest across large areas of the region. Elevated fire levels in the western United States have been correlated to changing climatic conditions, and if recent fire patterns persist, preservation of older forests in dry ecosystems will depend upon practical and coordinated fire management across the landscape.