The objectives of the effectiveness monitoring plan for the marbled murrelet (Brachyramphus marmoratus) include mapping nesting habitat at the start of the Northwest Forest Plan (NWFP) and estimating changes in that habitat every 5 years. Using Maxent species distribution models, we modeled the amount and distribution of probable nesting habitat in the murrelet’s range in the NWFP area in 1993, 1 year prior to the start of the NWFP, and 25 years later (2017). Within the higher probability nesting habitat, we then estimated the amount of contiguous habitat (core) versus the amount of habitat bounding core habitat (edge) and habitat scattered in small forest fragments (scatter). We considered this “core habitat” as the best habitat. Our models indicate that there were 1.51 million acre of higher probability nesting habitat over all lands in the murrelet’s range in Washington, Oregon, and California 1 year prior to the start of the NWFP in 1993. Of this, 0.14 million acre were identified as core habitat, which we defined as intact patches of higher probability nesting habitat >5.56 acre in size. In core habitat, we expected nest predation to be relatively low and the microclimate most favorable for murrelets. Most (68 percent, or 1.04 million acre) higher probability nesting habitat in 1993 was on federally administered lands, with 0.97 million acre (66 percent) in reserved land use allocations. We estimated that nonfederal lands contained 29 percent of all higher probability nesting habitat, but only 13 percent of all core habitat. Thus, the bulk of core habitat was on federal lands. We estimated a net loss of about 1.4 percent in higher probability nesting habitat across the NWFP area and 1.8 percent in core habitat from 1993 to 2017. Timber harvest and wildfire were the major causes of habitat loss on federal lands since the NWFP was implemented. Timber harvest was the primary cause of loss on state and other nonfederal lands, accounting for 99 percent of all attributable losses since 1993. The NWFP has been successful in conserving higher probability nesting habitat on federal lands across the NWFP area, but has been less successful in conserving core habitat. We anticipate that losses of habitat on federal lands will continue because of fires and timber harvest. As forests mature, some of these losses may be exceeded by recovery of currently unsuitable habitat within reserves. However, climate change offers a very real threat, and thus many gains may not be realized as the climate in the NWFP area becomes warmer, drier, and less favorable for developing forest conditions necessary for nesting murrelets. In addition, because losses of nesting habitat continue on private lands, incentives are needed to curb losses to better meet conservation objectives.
We show that aerial tips are self-similar fractals of whole shrubs and present a field method that applies this fact to improves accuracy and precision of biomass estimates of tall-shrubs, defined here as those with diameter at root collar (DRC) ≥ 2.5 cm. Power function allometry of biomass to stem diameter generates a disproportionate prediction error that increases rapidly with diameter. Thus, biomass should be modeled as a single measure of stem diameter only if stem diameter is less than a threshold Dmax. When stem diameter exceeds Dmax, then the stem internode should be treated as a conic frustrum requiring two additional measures: a second, node-adjacent diameter and a length. If the second diameter is less than Dmax, then the power function allometry can be applied to the aerial tip; otherwise an additional internode is measured. This "two-component" allometry-internodes as frustra and aerial tips as shrubs-can reduce estimated biomass error propagated to the plot-level by as much as 50% or more where very large shrubs are present Dmax is any diameter such that the ratio of single-component to two-component uncertainty exceeds the ratio of two-component to single-component measurement time. Guidelines for estimating Dmax based on pilot field data are provided. Tall shrubs are increasing in abundance and distribution across Arctic, alpine, boreal, and dryland ecosystems. Estimating their biomass is important for both ecological studies and carbon accounting. Reducing field-sample prediction error increases precision in multi-stage modeling because additional measures efficiently improve plot-level biomass precision, reducing uncertainty for shrub biomass estimates.
Forest structure and composition regulate a range of ecosystem services, including biodiversity, water and nutrient cycling, and wood volume for resource extraction. Forest type is an important metric measured in the US Forest Service Forest Inventory and Analysis (FIA) program, the national forest inventory of the USA. Forest type information can be used to quantify carbon and other forest resources within specific domains to support ecological analysis and forest management decisions, such as managing for disease and pests. In this study, we developed a methodology that uses a combination of airborne hyperspectral and lidar data to map FIA-defined forest type between sparsely sampled FIA plot data collected in interior Alaska. To determine the best classification algorithm and remote sensing data for this task, five classification algorithms were tested with six different combinations of raw hyperspectral data, hyperspectral vegetation indices, and lidar-derived canopy and topography metrics. Models were trained using forest type information from 632 FIA subplots collected in interior Alaska. Of the thirty model and input combinations tested, the random forest classification algorithm with hyperspectral vegetation indices and lidar-derived topography and canopy height metrics had the highest accuracy (78% overall accuracy). This study supports random forest as a powerful classifier for natural resource data. It also demonstrates the benefits from combining both structural (lidar) and spectral (imagery) data for forest type classification.
Currently, quantifying phenology at landscape to regional scales is not feasible with field data or near-surface sensors. Consequently, the spatial and temporal complexity of phenology has been assessed using satellite-based estimates (land surface phenology, LSP). While estimates from Moderate Resolution Imaging Spectroradiometer (MODIS) capture intraannual patterns of phenology, they have relatively low spatial resolution. Estimates from sensors like Landsat capture finer spatial detail but are often limited by Landsat’s temporal resolution. We implemented a spatio-temporal image fusion method on the Google Earth Engine (GEE) platform and used the resulting dense time series of images to estimate intraannual LSP at 30-meter resolution. We utilized Landsat 8 surface reflectance and MODIS NBAR (Nadir BRDF-Adjusted Reflectance; MCD43A4) images from 2016 and 2017 in the interior Pacific Northwest of the United States. Images predicted from the GEE image fusion algorithm were evaluated with true Landsat observations and compared with the accuracy achieved by executing the original ESTARFM algorithm. Excluding snow and cloud obscured observations, the algorithm produced approximately 215 observations per 30-meter pixel in 2017. Root mean squared prediction error (RMSPE) of Normalized Difference Vegetation Index (NDVI) for the GEE predicted images ranged from 0.032 to 0.066, and the RMSPE for the original ESTARFM predicted images from the ranged from 0.027 to 0.064. Phenometric estimates were evaluated with near-surface sensors (PhenoCams) in shrubland, conifer, and agricultural sites and field observations of phenology in grassland, open-pine, and mixed-conifer sites. Although phenometric estimates were dissimilar at all PhenoCam sites, the general temporal pattern of the GEE image fusion and PhenoCam time series was often similar. The start of season derived from the GEE image fusion time series had closer correspondence to the PhenoCam-derived start of season at the shrubland site (13 days) than the agriculture and conifer sites. The end of season was closest at one of the conifer sites and the agriculture site (22 and 31 days, respectively). Trends of some of the field-based phenology observations aligned with phenometrics estimated from the image fusion time series. At the grassland and open-pine field sites, the phenometrics from GEE image fusion were associated with phenophase trends of dominant plant functional types. Though characterizing LSP within the interior Pacific Northwest remains a challenge, this study demonstrates that image fusion implemented in GEE can produce a densified time series capable of capturing seasonal trends in NDVI related to vegetation phenology, which can be used to estimate intraannual phenometrics.
Long-term management strategies are invoked once an invasive species has become established and spread beyond feasible limits for eradication or containment. Although an invasive species may be well-established in small to large geographical areas, prevention of its spread to non-affected areas (e.g., sites, regions, and cross-continent) through early detection and monitoring is an important management activity. The level for management of established invasive species in the United States has increasingly shifted to larger geographical scales in the past several decades. Management of an invasive fish may occur at the watershed level in the western States, with watershed levels defined by their hydrologic unit codes (HUC) ranging from 2 digits at the coarsest level to 8 digits at the finest level (USGS 2018). Invasive plant management within national forests, grasslands, and rangelands can be implemented at the landscape level (e.g., Chambers et al. 2014), although management can still occur at the stand or base level. Landscapes in this chapter refer to areas of land bounded by large-scale physiographic features integrated with natural or man-made features that govern weather and disturbance patterns and limit frequencies of species movement (Urban et al. 1987). These are often at a large physical scale, such as the Great Basin.