A Macroecological Analysis of SERA Derived Forest Heights and Implications for Forest Volume Remote Sensing

Individual trees have been shown to exhibit strong relationships between DBH, height and volume. Often such studies are cited as justification for forest volume or standing biomass estimation through remote sensing. With resolution of common satellite remote sensing systems generally too low to resolve individuals, and a need for larger coverage, these systems rely on descriptive heights, which account for tree collections in forests. For remote sensing and allometric applications, this height is not entirely understood in terms of its location. Here, a forest growth model (SERA) analyzes forest canopy height relationships with forest wood volume. Maximum height, mean, H100, and Lorey's height are examined for variability under plant number density, resource and species. Our findings, shown to be allometrically consistent with empirical measurements for forested communities world-wide, are analyzed for implications to forest remote sensing techniques such as LiDAR and RADAR. Traditional forestry measures of maximum height, and to a lesser extent H100 and Lorey's, exhibit little consistent correlation with forest volume across modeled conditions. The implication is that using forest height to infer volume or biomass from remote sensing requires species and community behavioral information to infer accurate estimates using height alone. SERA predicts mean height to provide the most consistent relationship with volume of the height classifications studied and overall across forest variations. This prediction agrees with empirical data collected from conifer and angiosperm forests with plant densities ranging between 102–106 plants/hectare and heights 6–49 m. Height classifications investigated are potentially linked to radar scattering centers with implications for allometry. These findings may be used to advance forest biomass estimation accuracy through remote sensing. Furthermore, Lorey's height with its specific relationship to remote sensing physics is recommended as a more universal indicator of volume when using remote sensing than achieved using either maximum height or H100

Assessment of carbon in woody plants and soil across a vineyard-woodland landscape

<p>Abstract</p> <p>Background</p> <p>Quantification of ecosystem services, such as carbon (C) storage, can demonstrate the benefits of managing for both production and habitat conservation in agricultural landscapes. In this study, we evaluated C stocks and woody plant diversity across vineyard blocks and adjoining woodland ecosystems (wildlands) for an organic vineyard in northern California. Carbon was measured in soil from 44 one m deep pits, and in aboveground woody biomass from 93 vegetation plots. These data were combined with physical landscape variables to model C stocks using a geographic information system and multivariate linear regression.</p> <p>Results</p> <p>Field data showed wildlands to be heterogeneous in both C stocks and woody tree diversity, reflecting the mosaic of several different vegetation types, and storing on average 36.8 Mg C/ha in aboveground woody biomass and 89.3 Mg C/ha in soil. Not surprisingly, vineyard blocks showed less variation in above- and belowground C, with an average of 3.0 and 84.1 Mg C/ha, respectively.</p> <p>Conclusions</p> <p>This research demonstrates that vineyards managed with practices that conserve some fraction of adjoining wildlands yield benefits for increasing overall C stocks and species and habitat diversity in integrated agricultural landscapes. For such complex landscapes, high resolution spatial modeling is challenging and requires accurate characterization of the landscape by vegetation type, physical structure, sufficient sampling, and allometric equations that relate tree species to each landscape. Geographic information systems and remote sensing techniques are useful for integrating the above variables into an analysis platform to estimate C stocks in these working landscapes, thereby helping land managers qualify for greenhouse gas mitigation credits. Carbon policy in California, however, shows a lack of focus on C stocks compared to emissions, and on agriculture compared to other sectors. Correcting these policy shortcomings could create incentives for ecosystem service provision, including C storage, as well as encourage better farm stewardship and habitat conservation.</p

Above- and below-ground biomass accumulation, production, and distribution of sweetgum and loblolly pine grown with irrigation and fertilization.

Abstract: Increased forest productivity has been obtained by improving resource availability through water and nutrient amendments. However, more stress-tolerant species that have robust site requirements do not respond consistently to irrigation. An important factor contributing to robust site requirements may be the distribution of biomass belowground, yet available information is limited. We examined the accumulation and distribution of above- and below-ground biomass in sweetgum (Liqrridambar sfyrac$lua L.) and loblolly pine (Pinus taeda L.) stands receiving irrigation and fertilization. Mean annual aboveground production after 4 years ranged from 2.4 to 5.1 ~g.ha-'.year' for sweetgum and from 5.0 to 6.9 ~g.ha-l.year-l for pine. Sweetgum responded positively to irrigation and fertilization with an additive response to irrigation + fertilization. Pine only responded to fertilization. Sweetgum root mass fraction (RME)in creased with fertilization at 2 years and decreased with fertilization at 4 years. There were no detectable treatment differences in loblolly pine RMF. Development explained from 67% to 98% of variation in shoot versus root allometry for ephemeral and perennial tissues, fertilization explained no more than 5% of the variation in for either species, and irrigation did not explain any. We conclude that shifts in allocation from roots to shoots do not explain nutrient-induced growth stimulations

Stochastically generating tree diameter lists to populate forest stands based on the linkage variables, forest type and stand age.

Forest inventory data were used to develop a stand-age-driven, stochastic predictor of unit-area, frequency-weighted lists of breast high tree diameters (DBH). The average of mean statistics from 40-simulation prediction sets of an independent 78-plot validation dataset differed from the observed validation means by 0.5 cm for DBH, and by 12 trees/h for density. The 40-simulation average of standard deviation, quartile range, maximum value and minimum value differed from the validation dataset, respectively, by 0.3, 1.3, 0.6 and 1.5 cm for DBH, and 10, 42, 29, and 54 trees/h for density. In addition, test statistics were also computed individually for each of the 40 single simulations of the 78-plot validation dataset. In all cases, the test statistics supported the null hypothesis of no difference between simulated and observed DBH lists. When power of these hypothesis test statistics was set to 80%, the calculated minimum detectable differences were still reasonably small at 2.7 cm for mean DBH and 90 trees/h for stocking. Also, the shape and dispersion of simulated mean-DBH/density scatter graphs were similar to the same scatter graph from the observed, validation dataset

Soil compaction and chestnut ink disease

Chestnut ink disease caused by the oomycete Phytophthora cinnamomi, a soil-borne pathogen of world-wide distribution, accounts for the majority of disease problems on chestnuts in Portugal, limiting yield in a large number of stands and impeding establishment of trees in new areas. A survey was carried out in 32 chestnut stands in the Padrela Mountains of northern Portugal to investigate the relationship among ink disease occurrence, edaphic factors and management practices. A logistic regression function was employed to analyze the effect of soil attributes and management practices on the stand health status. Results showed that the main factors affecting disease were soil compaction (COMP), soil organic matter level (OM) and manuring practice (MA). A logistic model containing the soil variable COMP and the interaction term OM · MA correctly predicted the stand health status in 94%, or 30, of the 32 stands studied. The logistic function coefficients indicate that the probability of a stand having ink disease increases with increasing soil compaction and increasing soil organic matter content in stands where manuring is the usual practice.