Quantifying Early-Seral Forest Composition with Remote Sensing

Spatially explicit modeling of recovering forest structure within two years following wildfire disturbance has not been attempted, yet such knowledge is critical for determining successional pathways. We used remote sensing and field data, along with digital climate and terrain data, to model and map early-seral aspen structure and vegetation species richness following wildfire. Richness was the strongest model (RMSE = 2.47 species, Adj. R2 = 0.60), followed by aspen stem diameter, basal area (BA), height, density, and percent cover (Adj. R2 range = 0.22 to 0.53). Effects of pre-fire aspen BA and fire severity on post-fire aspen structure and richness were analyzed. Post-fire recovery attributes were not significantly related to fire severity, while all but percent cover and richness were sensitive to pre-fire aspen BA (Adj. R2 range = 0.12 to 0.33, p \u3c0.001). This remote mapping capability will enable improved prediction of future forest composition and structure, and associated carbon stocks

Reconstructing past forest composition and abundance by using archived Landsat and national forest inventory data

Effective modelling of forest susceptibility to defoliating insect outbreaks requires a better understanding of outbreak dynamics, which includes detailed knowledge of the pre- and post-outbreak forest status as well as subsequent feedback mechanisms. In this paper, we strive to fill the forest status need by combining archived Landsat sensor data (pre- and post-outbreak) with different formats and dates of the U.S. Forest Service’s Forest Inventory and Analysis (FIA) data (periodic [1970s, 1990s] and annual [2003–2006]). Specifically, we explore the utility of these FIA ground data for calibrating models of forest species and type abundance for mapping past forest composition in the Border Lakes Ecoregion (BLE) of Upper Midwest of the US. Model calibration results between Landsat reflectance and FIA ground data for both total forest basal area and balsam fir (Abies balsamea) relative basal area, a preferred host of the spruce budworm (SBW, Choristoneura fumiferana), were poor to moderate (R2adj 0.39 and 0.48, respectively). Results for aspen (Populous tremuloides) and spruce (Picea glauca and P. mariana) abundance yielded substantially better accuracies (R2adj 0.64 and 0.78; RMSE 15.56 and 10.65 m2 ha−1, respectively). Groupings of tree species into broadleaved and conifers substantially improved model calibration result (R2adj range: 0.72–0.91), except for the SBW host group (A. balsamea, P. glauca, and P. mariana). Periodic FIA ground data from the early 1990s generated stronger models compared to other FIA-Landsat date combinations tested. A paired t-test of abundance differences between undisturbed forest from the older 1977 and 1990 periodic inventories was significant (p-value \u3c 0.0001), suggesting possible effects of variable FIA sampling protocol or ground plot positional accuracy through time. However, a similar paired t-test of abundance difference between periodic FIA (1990) and annual FIA (2003–2006) was not significant (p-value = 0.249). We posit four potential factors that may have contributed to weak Landsat-FIA calibration results for species abundance: 1) variation in FIA subplot arrangement and sampling protocols through time, 2) variability in species abundance and heterogeneity among FIA sampling across adjacent Landsat orbital paths, 3) understory species (balsam fir) that are largely hidden from remote detection, and 4) cloud cover and orbital phase mismatches preventing capture of key forest phenology aids. While past and present FIA sampling protocols were not specifically designed for integration with 30-meter satellite sensor data, careful pairing of FIA ground data (past or present) with Landsat sensor data can facilitate reasonable estimates, of forest abundance for generalized forest types, and possibly forest species when heterogeneity is low. Nevertheless, we recommend that FIA subplot sampling protocols be augmented to include measurements of forest conditions that are more amenable to integration with 30-meter Landsat sensor data

Satellite-Based Management Tool for Oak Savanna Ecosystem Restoration

The structure and function of oak Quercus spp. savanna ecosystems in the North American Midwest were originally maintained by an active disturbance regime (often fire). Subsequent reductions in the frequency of disturbance after European settlement have facilitated rapid, regional conversion of these ecosystems to more closed-canopy forest. Hence, regional-scale management strategies are now needed to restore critical spatial gradients of light, temperature, soil moisture, and soil organic matter for recovery and sustenance of the unique mosaic of understory grass and forb species assemblages that define oak savannas. Tree species composition, distribution, mortality, basal area, and canopy cover are important forest structural parameters that are intrinsically linked to oak savanna restoration ecology. In this benchmark study, we seek to determine whether Landsat-based monitoring protocols can be developed as a tool to guide and monitor regional-scale restoration and management efforts. Using the Sherburne National Wildlife Refuge in central Minnesota as a test case, ground-based forest-structure data were collected and used with multitemporal Landsat sensor data and iterative exclusion partial least-squares regression to calibrate six predictive overstory structure models. Model calibrations produced moderate- to high-accuracy results with respective adjusted coefficient of determination and root mean-squared error values as follows: 0.859, 9.3% (canopy cover); 0.855, 2.95 m2 ha−1 (total basal area); 0.741, 11.6% (red oaks relative basal area); 0.781, 11.9% (bur oak relative basal area); 0.861, 3.20 m2 ha−1 (living oak basal area); and 0.833, 9.1% (dead oak relative basal area). We used the resulting structure models for the Sherburne test site to demonstrate how these data could be applied to help managers prioritize areas within management zones for restorative treatments. Although our Sherburne oak savanna test ecosystem is small (12,424 ha) compared with the size of a full Landsat scene (3.4 million ha), resulting structure models can be extended to the whole Landsat scene, which demonstrates how such modeling protocols can be used for repeated (e.g., annual to decadal), regional-scale analysis and assessment to improve management, planning, and implementation of oak savanna restoration efforts elsewhere

Comparing modern and presettlement forest dynamics of a subboreal wilderness: Does spruce budworm enhance fire risk?

Insect disturbance is often thought to increase fire risk through enhanced fuel loadings, particularly in coniferous forest ecosystems. Yet insect disturbances also affect successional pathways and landscape structure that interact with fire disturbances (and vice-versa) over longer time scales. We applied a landscape succession and disturbance model (LANDIS-II) to evaluate the relative strength of interactions between spruce budworm (Choristoneura fumiferana) outbreaks and fire disturbances in the Boundary Waters Canoe Area (BWCA) in northern Minnesota (USA). Disturbance interactions were evaluated for two different scenarios: presettlement forests and fire regimes vs. contemporary forests and fire regimes. Forest composition under the contemporary scenario trended toward mixtures of deciduous species (primarily Betula papyriferaand Populus spp.) and shade-tolerant conifers (Picea mariana, Abies balsamea, Thuja occidentalis), with disturbances dominated by a combination of budworm defoliation and high-severity fires. The presettlement scenario retained comparatively more “big pines” (i.e., Pinus strobus, P. resinosa) and tamarack (L. laricina), and experienced less budworm disturbance and a comparatively less-severe fire regime. Spruce budworm disturbance decreased area burned and fire severity under both scenarios when averaged across the entire 300-year simulations. Contrary to past research, area burned and fire severity during outbreak decades were each similar to that observed in non-outbreak decades. Our analyses suggest budworm disturbances within forests of the BWCA have a comparatively weak effect on long-term forest composition due to a combination of characteristics. These include strict host specificity, fine-scaled patchiness created by defoliation damage, and advance regeneration of its primary host, balsam fir (A. balsamea) that allows its host to persist despite repeated disturbances. Understanding the nature of the three-way interaction between budworm, fire, and composition has important ramifications for both fire mitigation strategies and ecosystem restoration initiatives. We conclude that budworm disturbance can partially mitigate long-term future fire risk by periodically reducing live ladder fuel within the mixed forest types of the BWCA but will do little to reverse the compositional trends caused in part by reduced fire rotations

Vegetation recovery following fire and harvest disturbance in central Labrador — a landscape perspective

Understanding vegetation recovery patterns following wildfire and logging disturbance is essential for long-term planning in sustainable forestry. Plot-scale studies indicate differences in revegetation rates and postdisturbance composition in Labrador, Canada, following fire in comparison with harvest but do not necessarily capture the full range of relevant landscape variability. Using a satellite-based land cover classification that distinguishes forest, woodland, shrub, lichen, and bare ground, we applied partial least-squared regression (PLS) to derive empirical models of vegetation dynamics following fire and harvest. Forest recovery rates were found to be generally slow and sensitive to predisturbance land condition and site quality (potential productivity). We found that, although disturbance type was not specifically retained in the model, estimated rates of vegetation recovery were faster for a typical harvest compared with a typical fire (i.e., 50% recovery at 14 years versus 33 years, respectively). Indeed, the model predicts important regeneration delay following fire that appears sensitive to both site quality and area burned. Understanding factors affecting broad-scale vegetation recovery relationships can help guide future sustainable forestry and wildlife habitat initiatives in the region, in part by parameterizing landscape simulation models used for strategic decision support

Characterizing and Comprehending Land Use Change in the Loess Hills Region

Regional land use change has important implications for ecosystems and the local human population. Metropolitan areas (MAs) are placing increasing emphasis on amenities and the environment when seeking to attract high income workers and their employers. Our interest is in characterizing land use change in Iowa’s Loess Hills Ecoregion (ILHE) that skirts both Sioux City and Council Bluffs MAs. ILHE is a distinctive landform of silty soils up to 200 feet high that were wind deposited just east of the Missouri River floodplain. Covering about 0.7 million acres, the Loess hills stretch north about 200 miles (usually no wider than 15 miles) from Holt County, Missouri, to Plymouth County, Iowa and are largely under private ownership. Although the soils are rich, cultivation has been difficult so that the region contains more than 50 percent of Iowa’s remnant prairie. However, technologies that allow cropping on steeply sloped and highly erodible terrains, increasing agricultural prices, and pressure for urban development have led to concerns about habitat loss conversion and fragmentation (Farnsworth et al. 2010)

Forest land cover change (1975-2000) in the Greater Border Lakes region

This document and accompanying maps describe land cover classifications and change detection for a 13.8 million ha landscape straddling the border between Minnesota, and Ontario, Canada (greater Border Lakes Region). Land cover classifications focus on discerning Anderson Level II forest and nonforest cover to track spatiotemporal changes in forest cover. Multi-temporal Landsat Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Multi-Spectral Scanner (MSS) data from 1972 to 2000 were used to classify forest cover types and disturbances at 5-year intervals. A composite dataset depicting the period of forest disturbance was produced using the 1975-2000 sequence of land cover data. These land cover change data were produced to facilitate analysis of forest disturbance patterns, to support landscape simulation modeling, and to support cross-ownership land management within the region. A double-sided fold-out map shows A) forest land cover change across differently managed forests, and B) classified period of forest canopy disturbance for the entire study area. Digital versions of the map are available online, as are the datasets and code used to produce them

Land Use Change and Policy in Iowa’s Loess Hills

We consider land use change in Iowa’s Loess Hills, which contain much of the state’s remaining prairie grassland. Although crop production has expanded on the landform since 2005, much of this expansion has been from soybean into corn with a clear trend toward more intensive corn rotations. Forest land has expanded in the area while we do not find evidence of extensive conversion to development. Data indicate that crop production has moved away from more heavily sloped land, but the increase in cropping does not appear to be occurring on land with high crop productivity

Importance of scale, land cover, and weather on the abundance of bird species in a managed forest

Climate change and habitat loss are projected to be the two greatest drivers of biodiversity loss over the coming century. While public lands have the potential to increase regional resilience of bird populations to these threats, long-term data are necessary to document species responses to changes in climate and habitat to better understand population vulnerabilities. We used generalized linear mixed models to determine the importance of stand-level characteristics, multi-scale land cover, and annual weather factors to the abundance of 61 bird species over a 20-year time frame in Chippewa National Forest, Minnesota, USA. Of the 61 species modeled, we were able to build final models with R-squared values that ranged from 26% to 69% for 37 species; the remaining 24 species models had issues with convergence or low explanatory power (R-squared \u3c 20%). Models for the 37 species show that stand-level characteristics, land cover factors, and annual weather effects on species abundance were species-specific and varied within guilds. Forty-one percent of the final species models included stand-level characteristics, 92% included land cover variables at the 200 m scale, 51% included land cover variables at the 500 m scale, 46% included land cover variables at the 1000 m scale, and 38% included weather variables in best models. Three species models (8%) included significant weather and land cover interaction terms. Overall, models indicated that aboveground tree biomass and land cover variables drove changes in the majority of species. Of those species models including weather variables, more included annual variation in precipitation or drought than temperature. Annual weather variability was significantly more likely to impact abundance of species associated with deciduous forests and bird species that are considered climate sensitive. The long-term data and models we developed are particularly suited to informing science-based adaptive forest management plans that incorporate climate sensitivity, aim to conserve large areas of forest habitat, and maintain an historical mosaic of cover types for conserving a diverse and abundant avian assemblage