Over the past three decades, wildfires in southwestern US ponderosa pine (Pinus ponderosa Lawson & C. Lawson) forests have increased in size and severity. These wildfires can remove large, contiguous patches of mature forests, alter dominant plant communities and increase woody debris, potentially altering fungal community composition. Additionally, post-fire conditions may shift dominant fungal functional groups from plantsymbiotic ectomycorrhizal (EM) fungi to more decomposer saprotrophic fungi. We investigated the long-term (13 years post-wildfire) effect of fire severity on 1) fungal sporocarp density, functional groups and community composition and 2) EM colonization and community composition from naturally regenerating ponderosa pine seedlings on the Pumpkin Fire that burned in 2000 in Arizona, USA. Plots were located in four burn severity classes: unburned, moderate-severity, and two high-severity (defined as 100% tree mortality) classes, either adjacent to residual live forest edges (edge plots), or > 200 m from any residual live trees (interior plots). We found that high-severity burn plots had a unique sporocarp community composition, and a shift in dominant sporocarp functional groups, with 5-13 times lower EM sporocarp densities, and 4-7 times lower EM sporocarp species richness compared to unburned and moderate-severity plots. In contrast, saprotrophic sporocarp densities and richness were similar among burn severity classes, even with the large amount of coarse wood in the highseverity burn patches. Regenerating ponderosa pine seedlings had similar EM colonization and richness among severity classes, yet high-severity interior plots had a different community composition and a lower relative abundance of EM species compared to moderate-severity burn plots. Taken together, our results suggest that large patches of high-severity fire have long-term consequences for both EM sporocarp and root tip communities. Because EM fungal species vary in function, the limited species pool available in interior high-severity burn patches may influence pine recovery.
The Qinghai-Tibet Plateau constitutes unique mountain ecosystems that can be used for early detection of the impacts of climate change on ecosystem functions. We use the MAPSSCENTURY 2 (MC2), a dynamic global vegetation model, to examine the potential responses of terrestrial ecosystems to climate change in the past (1961–2010) and future (2011–2080) under one medium-low warming scenario (RCP4.5) at a 1-km spatial resolution in the Upper Heihe River Basin (UHRB), northwestern China. Results showed that 21.4% of the watershed area has experienced changes in potential natural vegetation types in the past and that 42.6% of the land would undergo changes by the 2070s, characterized by a sharp increase in alpine tundra at the cost of cold barren land. Net primary productivity (NPP) and heterotrophic respiration (RH) have increased sharply since the mid-1980s and are projected to remain at reduced rates in the future. Overall, UHRB switched from carbon neutral to a carbon sink in 1961–2010, and the sink strength is projected to decline after 2040. Additionally, future climate change is projected to drive a decrease in water yield due to a slight decrease in precipitation and an increase in evapotranspiration (ET). Furthermore, we find large spatial variations in simulated ecosystem dynamics, including an upward trend of NPP, RH, and ET in the alpine zone, but a downward trend in themid-elevation forest zone. These results underscore the complexity of potential impacts of climate change on mountain watersheds that represent the headwaters of inland river systems in an arid environment.
Photogrammetry-based three-dimensional reconstruction of objects is becoming increasingly appealing in research areas unrelated to computer vision. It has the potential to facilitate the assessment of forest inventory-related parameters by enabling or expediting resource measurements in the field. We hereby compare several implementations of photogrammetric algorithms (CMVS/PMVS, CMPMVS, MVE, OpenMVS, SURE and Agisoft PhotoScan) with respect to their performance in vegetation assessment. The evaluation is based on (i) a virtual scene where the precise location and dimensionality of objects is known a priori and is thus conducive to a quantitative comparison and (ii) using series of in situ acquired photographs of vegetation with overlapping field of view where the photogrammetric outcomes are compared qualitatively. Performance is quantified by computing receiver operating characteristic curves that summarize the type-I and type-II errors between the reference and reconstructed tree models. Similar artefacts are observed in synthetic- and in situ-based reconstructions.
Forest inventories are constrained by resource-intensive fieldwork, while unmanned aerial systems (UASs) offer rapid, reliable, and replicable data collection and processing. This research leverages advancements in photogrammetry and market sensors and platforms to incorporate a UAS-based approach into existing forestry monitoring schemes. Digital imagery from a UAS was collected, photogrammetrically processed, and compared to in situ and aerial laser scanning (ALS)-derived plot tree counts and heights on a subsample of national forest plots in Oregon. UAS- and ALS-estimated tree counts agreed with each other (r2 = 0.96) and with field data (ALS r2 = 0.93, UAS r2 = 0.84). UAS photogrammetry also reasonably approximated mean plot tree height achieved by the field inventory (r2 = 0.82, RMSE = 2.92 m) and by ALS (r2 = 0.97, RMSE = 1.04 m). The use of both nadir-oriented and oblique UAS imagery as well as the availability of ALS-derived terrain descriptions likely sustain a robust performance of our approach across classes of canopy cover and tree height. It is possible to draw similar conclusions from any of the methods, suggesting that the efficient and responsive UAS method can enhance field measurement and ALS in longitudinal inventories. Additionally, advancing UAS technology and photogrammetry allows diverse users access to forest data and integrates updated methodologies with traditional forest monitoring.
Atmospheric nitrogen and sulfur pollution increased over much of the United States during the twentieth century from fossil fuel combustion and industrial agriculture. Despite recent declines, nitrogen and sulfur deposition continue to affect many plant communities in the United States, although which species are at risk remains uncertain. We used species composition data from >14,000 survey sites across the contiguous United States to evaluate the association between nitrogen and sulfur deposition and the probability of occurrence for 348 herbaceous species. We found that the probability of occurrence for 70% of species was negatively associated with nitrogen or sulfur deposition somewhere in the contiguous United States (56% for N, 51% for S). Of the species, 15% and 51% potentially decreased at all nitrogen and sulfur deposition rates, respectively, suggesting thresholds below the minimum deposition they receive. Although more species potentially increased than decreased with nitrogen deposition, increasers tended to be introduced and decreasers tended to be higher-value native species. More vulnerable species tended to be shorter with lower tissue nitrogen and magnesium. These relationships constitute predictive equations to estimate critical loads. These results demonstrate that many herbaceous species may be at risk from atmospheric deposition and can inform improvements to air quality policies in the United States and globally.
Low-cost methods to measure forest structure are needed to consistently and repeatedly inventory forest conditions over large areas. In this study we investigate low-cost pushbroom Digital Aerial Photography (DAP) to aid in the estimation of forest volume over large areas in Washington State (USA). We also examine the effects of plot location precision (low versus high) and Digital Terrain Model (DTM) resolution (1 m versus 10 m) on estimation performance. Estimation with DAP and post-stratification with high-precision plot locations and a 1 m DTM was 4 times as efficient (precision per number of plots) as estimation without remote sensing and 3 times as efficient when using low-precision plot locations and a 10 m DTM. These findings can contribute significantly to efforts to consistently estimate and map forest yield across entire states (or equivalent) or even nations. The broad-scale, high-resolution, and high-precision information provided by pushbroom DAP facilitates used by a wide variety of user types such a towns and cities, small private timber owners, fire prevention groups, Non-Governmental Organizations (NGOs), counties, and state and federal organizations.
Conservation planning for wildlife species requires mapping and assessment of habitat suitability across broad areas, often relying on a diverse suite, or stack, of geospatial data presenting multidimensional controls on a species. Stacks of univariate, independently developed vegetation layers may not represent relationships between each variable that can be characterized by multivariate modeling techniques, leading to inaccurate inferences on the distribution of suitable habitat. In this paper, we examine the role of variable combining in mapping multiple dimensions of greater sage-grouse (Centrocercus urophasianus, GRSG) habitat as a basis for GRSG conservation in the great basin ecoregion within southeastern Oregon. We compare two modeling approaches: a univariate random forest regression model (RF regression) and a multivariate random forest nearest neighbor (RFNN) imputation model , across an array of variables. These include five GRSG habitat descriptor variables: percent cover of trees, juniper, sagebrush, and GRSG food forbs, and the proportion of grasses that are exotic annuals. We also model species distributions of 51 common species in the sage steppe and combine these predictions to estimate alpha diversity. Our results show that RF regression and RFNN can yield univariate predictions with similar performance, but RF regression predictions tend to contain slightly more bias at broader spatial scales. Stacking univariate predictions from RF regression yields covariance errors that manifest as logical errors (juniper cover > tree cover), biases in estimates of GRSG habitat area, and biases in estimates of alpha diversity. Combining variables from the RFNN model does not introduce covariance errors. We conclude that multivariate modeling approaches are better suited to map multidimensional habitat niches at broader spatial scales, and also better suited to provide information for defining multivariable adaptive management triggers at the population level or above.
Recent increases in forest diseases have produced significant mortality in boreal forests. These disturbances influence merchantable volume predictions as they affect the distribution of live and dead trees. In this study, we assessed the use of lidar, alone or combined with multispectral imagery, to classify trees and predict the merchantable volumes of 61 balsam fir plots in a boreal forest in eastern Canada. We delineated single trees on a canopy height model. The number of detected trees represented 92% of field trees. Using lidar intensity and image pixel metrics, trees were classified as live or dead with an overall accuracy of 89% and a kappa coefficient of 0.78. Plots were classified according to their class of mortality (low/high) using a 10.5% threshold. Lidar returns associated with dead trees were clipped. Before clipping, the root mean square errors were of 22.7 m3 ha-1 in the low mortality plots and of 39 m3 ha-1 in the high mortality plots. After clipping, they decreased to 20.9 m3 ha-1 and 32.3 m3 ha-1 respectively. Our study suggests that lidar and multispectral imagery can be used to accurately filter dead balsam fir trees and decrease the merchantable volume prediction error by 17.2% in high mortality plots and by 7.9% in low mortality plots.