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’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. Full article

National monitoring of forestlands and the processes causing canopy cover loss, be they abrupt or gradual, partial or stand clearing, temporary (disturbance) or persisting (deforestation), are necessary at fine scales to inform management, science and policy. This study utilizes the Landsat archive and an ensemble of disturbance algorithms to produce maps attributing event type and timing to >258 million ha of contiguous Unites States forested ecosystems (1986–2010). Nationally, 75.95 million forest ha (759,531 km²) experienced change, with 80.6% attributed to removals, 12.4% to wildfire, 4.7% to stress and 2.2% to conversion. Between regions, the relative amounts and rates of removals, wildfire, stress and conversion varied substantially. The removal class had 82.3% (0.01 S.E.) user’s and 72.2% (0.02 S.E.) producer’s accuracy. A survey of available national attribution datasets, from the data user’s perspective, of scale, relevant processes and ecological depth suggests knowledge gaps remain. Full article

In this paper, we propose a new estimator for creating expansion factors for survey plots in the US Forest Service (USFS) Forest Inventory and Analysis program. This estimator was previously used in the GIS literature, where it was called Penalized Maximum Entropy Dasymetric Modeling. We show here that the method is a regularized version of the raking estimator widely used in sample surveys. The regularized raking method differs from other predictive modeling methods for integrating survey and ancillary data, in that it produces a single set of expansion factors that can have a general purpose which can be used to produce small-area estimates and wall-to-wall maps of any plot characteristic. This method also differs from other more widely used survey techniques, such as GREG estimation, in that it is guaranteed to produce positive expansion factors. Here, we extend the previous method to include cross-validation, and provide a comparison to expansion factors between the regularized raking and ridge GREG survey calibration. Full article

Understanding change in forest carbon (C) is important for devising strategies to reduce emissions of greenhouse gases. National forest inventories (NFIs) are important to meet international accounting goals, but data are often incomplete going back in time, and the amount of time between remeasurements can make attribution of C flux to specific events difficult. The long time series of Landsat imagery provides spatially comprehensive, consistent information that can be used to fill the gaps in ground measurements with predictive models. To evaluate such models, we relate Landsat spectral changes and disturbance interpretations directly to C flux measured on NFI plots and compare the performance of models with and without ground-measured predictor variables. The study was conducted in the forests of southwest Oregon State, USA, a region of diverse forest types, disturbances, and landowner management objectives. Plot data consisted of 676 NFI plots with remeasured individual tree data over a mean interval (time 1 to time 2) of 10.0 years. We calculated change in live aboveground woody carbon (AWC), including separate components of growth, mortality, and harvest. We interpreted radiometrically corrected annual Landsat images with the TimeSync (TS) tool for a 90 m × 90 m area over each plot. Spectral time series were divided into segments of similar trajectories and classified as disturbance, recovery, or stability segments, with type of disturbance identified. We calculated a variety of values and segment changes from tasseled cap angle and distance (TCA and TCD) as potential predictor variables of C flux. Multiple linear regression was used to model AWC and net change in AWC from the TS change metrics. The TS attribution of disturbance matched the plot measurements 89% of the time regarding whether fire or harvest had occurred or not. The primary disagreement was due to plots that had been partially cut, mostly in vigorous stands where the net change in AWC over the measurement was positive in spite of cutting. The plot-measured AWC at time 2 was 86.0 ± 78.7 Mg C ha⁻¹ (mean and standard deviation), and the change in AWC across all plots was 3.5 ± 33 Mg C ha⁻¹ year⁻¹. The best model for AWC based solely on TS and other mapped variables had an R² = 0.52 (RMSE = 54.6 Mg C ha⁻¹); applying this model at two time periods to estimate net change in AWC resulted in an R² = 0.25 (RMSE = 28.3 Mg ha⁻¹) and a mean error of −5.4 Mg ha⁻¹. The best model for AWC at time 2 using plot measurements at time 1 and TS variables had an R² = 0.95 (RSME = 17.0 Mg ha⁻¹). The model for net change in AWC using the same data was identical except that, because the variable being estimated was smaller in magnitude, the R² = 0.73. All models performed better at estimating net change in AWC on TS-disturbed plots than on TS-undisturbed plots. The TS discrimination of disturbance between fire and harvest was an important variable in the models because the magnitude of spectral change from fire was greater for a given change in AWC. Regional models without plot-level predictors produced erroneous predictions of net change in AWC for some of the forest types. Our study suggests that, in spite of the simplicity of applying a single carbon model to multiple image dates, the approach can produce inaccurate estimates of C flux. Although models built with plot-level predictors are necessarily constrained to making predictions at plot locations, they show promise for providing accurate updates or back-calculations of C flux assessments. Full article

In China, the Returning Farmland to Forest Program (RFFP) has afforested large areas, transforming land and livelihoods. By impacting vegetation cover, it may also drive spatial pattern changes across landscapes. Most studies have focused on time series data as a means to determine the effectiveness of the program, but there is a paucity of community-level comparative studies. Twelve communities in Northwest Yunnan Province were selected to test whether the RFFP changed landscape patterns by testing the following hypotheses: with (or without) the RFFP, forest and shrubland fragmentations would decrease (or increase) and farmland fragmentation would increase (or decrease). Remote sensing images from 2000, 2010, and 2014 were used to compare the differences in landscape patterns. Survey data from 421 households were used to examine the socioeconomic and ecological factors that affect the differences in landscape fragmentation across communities. The results showed that landscape patterns and fragmentation metrics were not significantly different between communities with or without the RFFP, regardless of the class or landscape level. These communities showed consistent patterns of change in their fragmentation parameters between 2000 and 2014, with forest fragmentation decreasing and the fragmentation of farmland and the overall landscape increasing. The regression models suggest these changes were affected by the local natural conditions, socioeconomic patterns, policy implementation, and farmer livelihoods, with the proximity to market towns and elevation being significant factors. The RFFP alone did not directly drive the changes in landscape patterns for the considered region. For the new RFFP to effectively contribute to reducing fragmentation, managers of afforestation efforts should carefully consider livelihoods and biophysical factors that influence changes in landscape patterns. Full article

Forest fragmentation and degradation are a problem in many areas of the world and are a cause for concern to land managers. Similarly, countries interested in curtailing climate change have a keen interest in monitoring forest degradation. Traditional methods for measuring forested landscape pattern dynamics with maps made from classified satellite imagery fall short with respect to the compatibility of their forest definitions with information needs. In addition, they are not easily amenable to interpretation using tools like confidence intervals derived from survey sampling theory. In this paper, we described a novel landscape monitoring approach that helps fill these gaps. In it, a grid of photo plots is efficiently created and overlaid on high-resolution imagery, points are labeled with respect to their land-use by a human interpreter, and mean values and their variance are calculated for a suite of point-based fragmentation metrics related to forest degradation. We presented three case studies employing this approach from the US states of Maryland and Pennsylvania, highlighted different survey sampling paradigms, and discussed the strengths and weaknesses of the method relative to traditional, satellite imagery-based approaches. Results indicate that the scale of forest fragmentation in Maryland is between 250 and 1000 m, and this agrees with compatible estimates derived from raster analytical methods. There is a positive relationship between an index of housing construction and change in forest aggregation as measured by our metrics, and strong agreement between metric values collected by human interpretation of imagery and those obtained from a land cover map from the same period. We showed how the metrics respond to simulated degradation, and offered suggestions for practitioners interested in leveraging rapid photointerpretation for forest degradation monitoring. Full article

Wildfires in forest ecosystems produce landscape mosaics that include relatively unaffected areas, termed fire refugia. These patches of persistent forest cover can support fire-sensitive species and the biotic legacies important for post-fire forest recovery, yet little is known about their abundance and distribution within fire perimeters. Readily accessible 30-m resolution satellite imagery and derived burn severity products are commonly employed to characterize post-fire landscapes; however, coarse image resolution, generalized burn severity thresholds, and other limitations can constrain accurate representation of fire refugia. This study quantifies the abundance and pattern of fire refugia within 10 fires occurring in ponderosa pine and dry mixed-conifer forests between 2000 and 2003. We developed high-resolution maps of post-fire landscapes using semi-automated, object-based classification of 1-m aerial imagery, conducted imagery- and field-based accuracy assessments, and contrasted these with Landsat-derived burn severity metrics. Fire refugia area within burn perimeters ranged from 20% to 57%. Refugia proportion generally decreased with increasing Landsat-derived burn severity, but still accounted for 3–12% of areas classified as high severity. Patch size ranged from 1-m² isolated trees to nearly 8000 ha, and median patch size was 0.01 ha—substantially smaller than a 30-m Landsat pixel. Patch size was negatively related to burn severity; distance to fire refugia from open areas was positively related to burn severity. Finally, optimized thresholds of 30-m post-fire normalized burn ratio (NBR) and relative differenced normalized burn ratio (RdNBR) delineated fire refugia with an accuracy of 77% when validated against the 1-m resolution maps. Estimations of fire refugia abundance based on Landsat-derived burn severity metrics are unlikely to detect small, isolated fire refugia patches. Finer-resolution maps can improve understanding of the distribution of forest legacies and inform post-fire management activities including reforestation and treatments. Full article

Exact knowledge over tree growth is valuable information for decision makers when considering the purposes of sustainable forest management and planning or optimizing the use of timber, for example. Terrestrial laser scanning (TLS) can be used for measuring tree and forest attributes in very high detail. The study aims at characterizing changes in individual tree attributes (e.g., stem volume growth and taper) during a nine year-long study period in boreal forest conditions. TLS-based three-dimensional (3D) point cloud data were used for identifying and quantifying these changes. The results showed that observing changes in stem volume was possible from TLS point cloud data collected at two different time points. The average volume growth of sample trees was 0.226 m³ during the study period, and the mean relative change in stem volume was 65.0%. In addition, the results of a pairwise Student’s t-test gave strong support (p-value 0.0001) that the used method was able to detect tree growth within the nine-year period between 2008–2017. The findings of this study allow the further development of enhanced methods for TLS-based single tree and forest growth modeling and estimation, which can thus improve the accuracy of forest inventories and offer better tools for future decision-making processes. Full article