Fan-Rui Meng

A growth and self-thinning model for pure even-age stands: theory and applications

Abstract A self-thinning model is developed for fully stocked and under stocked pure even-aged stands. The self-thinning power law for fully stocked stands can be considered as a special case of this model. A stand growth model is developed by combining the self-thinning model with a basal-area increment model. This stand growth model can be used to estimate the average diameter and stand density at any given stand age with any initial stand conditions. The model was tested with yield table data. The model predictions were found to be agree with independent developed yield table data.

Spatial mapping of growing degree days: an application of MODIS-based surface temperatures and enhanced vegetation index

Growing degree days (GDD) is a simple temperature-based index of biological development. In this paper we evaluated the potential of using 2003-2005 MODIS-based 8-day and 16-day composites of daytime surface temperature (TS) and enhanced vegetation index (EVI) values at 250 m resolution for mapping GDD. The work was applied to the Canadian Atlantic Maritime Ecozone as a demonstration of the methodology. The work proceeded by establishing an empirical relationship between mean tower-based estimates of TS for the MODIS-acquisition period of 10:30 am-12:00 pm and the daily mean TS calculated from half-hourly emitted infrared/longwave radiation measurements taken from four flux sites in southern commercial forests of Canada. The relationship revealed a strong correlation between variables (r2=98.4%) and was central to the calculation of daily mean TS from MODIS-based estimates of TS. Since seasonally-based estimates of GDD and EVI were strongly correlated (r2=87%), data fusion techniques were applied to enhance the GDD map originally produced at 1 km resolution (from infrared emission band data), to 250 m. In general, the MODIS-derived map of GDD showed a positive constant offset of about 511 degree days from calculated long-term averages (1971 2000) based on temperatures collected at 101 Environment Canada climate stations.

Gaseous carbon dioxide and methane, as well as dissolved organic carbon losses from a small temperate wetland under a changing climate

Temperate forests can contain large numbers of wetlands located in areas of low relief and poor drainage. These wetlands can make a large contribution to the dissolved organic carbon (DOC) load of streams and rivers draining the forests, as well as the exchange of methane (CH4) and carbon dioxide (CO2) with the atmosphere. We studied the carbon budget of a small wetland, located in Kejimkujik National Park, Nova Scotia, Canada. The study wetland was the Pine Marten Brook site, a poor fen draining a mixed hardwood-softwood forest. We studied the loss of DOC from the wetland via the outlet stream from 1990 to 1999 and related this to climatic and hydrologic variables. We added the DOC export information to information from a previously published model describing CH4 and CO2 fluxes from the wetland as a function of precipitation and temperature, and generated a new synthesis of the major C losses from the wetland. We show that current annual C losses from this wetland amount to 0.6% of its total C mass. We then predicted that under climate changes caused by a doubling of atmospheric CO2 expected between 2040 and 2050, total C loss from the wetland will almost double to 1.1% of total biomass. This may convert this wetland from what we assume is currently a passive C storage area to an active source of greenhouse gases.

A Wetness Index Using Terrain-Corrected Surface Temperature and Normalized Difference Vegetation Index Derived from Standard MODIS Products: An Evaluation of Its Use in a Humid Forest-Dominated Region of Eastern Canada

In this paper we develop a method to estimate land-surface water content in a mostly forest-dominated (humid) and topographically-varied region of eastern Canada. The approach is centered on a temperature-vegetation wetness index (TVWI) that uses standard 8-day MODIS-based image composites of land surface temperature (TS) and surface reflectance as primary input. In an attempt to improve estimates of TVWI in high elevation areas, terrain-induced variations in TS are removed by applying grid, digital elevation model-based calculations of vertical atmospheric pressure to calculations of surface potential temperature (θS). Here, θS corrects TS to the temperature value to what it would be at mean sea level (i.e., ∼101.3 kPa) in a neutral atmosphere. The vegetation component of the TVWI uses 8-day composites of surface reflectance in the calculation of normalized difference vegetation index (NDVI) values. TVWI and corresponding wet and dry edges are based on an interpretation of scatterplots generated by plotting θS as a function of NDVI. A comparison of spatially-averaged field measurements of volumetric soil water content (VSWC) and TVWI for the 2003-2005 period revealed that variation with time to both was similar in magnitudes. Growing season, point mean measurements of VSWC and TVWI were 31.0% and 28.8% for 2003, 28.6% and 29.4% for 2004, and 40.0% and 38.4% for 2005, respectively. An evaluation of the long-term spatial distribution of land-surface wetness generated with the new θS-NDVI function and a process-based model of soil water content showed a strong relationship (i.e., r2 = 95.7%).

Simulations of pre- and post-harvest soil temperature, soil moisture, and snowpack for jack pine: comparison with field observations.

Quantifying temporal changes in soil temperature and moisture conditions is an important part of characterizing pre- and post-disturbance conditions that influence the health, productivity, and sustainability of forest ecosystems. In this paper, we present an experimental case study that was used to evaluate the ability of the forest hydrology model ForHyM2 to simulate field-observed changes in root-zone soil moisture and temperature, as well as snowpack depth, throughfall volume and forest floor percolate volume, for a jack pine (Pinus banksiana Lamb.) site in northeastern Ontario. The experiment refers to two post-harvest treatment factors, each involving two treatments: (a) blading (removing) or non-blading the forest floor and part of the mineral topsoil, (b) herbiciding or non-herbiciding. It was found that harvesting increased the average daily soil temperature by 4‐68C on all treatment plots during summer (5 cm soil depth). Blading increased the soil temperature further by 1‐28C. Herbiciding did not have significant effects on soil temperature. Eliminating competing forest vegetation significantly increased soil moisture level on the non-bladed treatment plots. The model simulations were based on daily precipitation (snow and rain), air temperature, and a few site descriptors such as longitude and latitude, soil depth, soil texture, and leaf area index. The resulting simulations compared well (graphically) with the pre- and post-harvest field observations regarding soil moisture, soil temperature, and snowpack water equivalents. Good graphical agreements suggest that the approach taken with this case study can be applied to the evaluation of soil moisture and temperature conditions to a variety of pre- and post-disturbance forest conditions. The results from the study would be useful for addressing below ground processes such as root growth, soil respiration, rate of organic matter decomposition, rate of soil weathering, nutrient cycling, etc., all of which strongly influence site productivity. # 2000 Elsevier Science B.V. All rights reserved.

The impact of initial stand density and site index on maximum stand density index and self-thinning index in a stand self-thinning model

Abstract A procedure was developed to test the impacts of the initial stand density and site index on the parameters of a previously established stand self-thinning model. The model being tested has four parameters: maximum density index, self-thinning index, maximum self-thinning rate and stand stocking deficiency coefficient. Data from a spacing experiment with Australian mountain ash ( Eucalyptus regnans F. Muell) were used to test the impact of initial stand density on the maximum density index and the self-thinning index. Data from a plantation experiment of Changbai larch ( Larix olgensis Henry) from China were used to test the impact of the site indices. It was found that both maximum density index and self-thinning index are not significantly affected by initial stand densities. No significant site index effects on these two parameters were detected with the limited data on the Changbai larch. It was found that the stocking deficiency coefficient is site specific and decreases with increasing initial stand density. The model predictions based on the estimated parameters were found to agree closely with field observations.

Modeling mass and nitrogen remaining in litterbags for Canadian forest and climate conditions

A new Forest Litter Decomposition Model (FLDM) is presented to simulate mass, N and carbon/nitrogen ratios (C/N) according to the 1992–1998 leaf litterbag data of the Canadian Intersite Decomposition Experiment (CIDET). This experiment involved 10 litter types, with litterbags placed on the ground of 18 upland and 3 wetland sites across Canada. The calibrated model based on first-order reaction kinetics calculates total mass, N concentration and C/N for each litter type and location using: three compartments (fast, slow, and very slow), four parameters for compartment initialization; three for compartment-based decay; three to assess the climate influence on decay; and one each to determine the rate o f N-mineralization and the final C/N ratio. With FLDM, the initial fast fraction is determined from the initial water-extractable and acid-hydrolyzable or acid-unhydrolyzable portions of the litter; the initial ash content determines the ratio between the slow and very slow fractions. Mean July and January a...

Using artificial neural network models to produce soil organic carbon content distribution maps across landscapes.

Soil organic carbon (SOC) content is an important soil quality indicator that plays an important role in regulating physical, chemical and biological properties of soil. Field assessment of SOC is time consuming and expensive. It is difficult to obtain high-resolution SOC distribution maps that are needed for landscape analysis of large areas. An artificial neural network (ANN) model was developed to predict SOC based on parameters derived from digital elevation model (DEM) together with soil properties extracted from widely available coarse resolution soil maps (1:1 000 000 scale). Field estimated SOC content data extracted from high-resolution soil maps (1:10 000 scale) in Black Brook Watershed in northwestern New Brunswick, Canada, were used to calibrate and validate the model. We found that vertical slope position (VSP) was the most important variable that determines distributions of SOC across the landscape. Other variables such as slope steepness, and potential solar radiation (PSR) also had signifi...

Influences of Sampling Methodologies on Pesticide-Residue Detection in Stream Water

Traditional grab sampling (GS) used widely in the study of water quality has been found lacking in spatial and temporal resolution for pesticide residue monitoring in stream water. The objectives of this article are to present a hydrograph-based sampling approach and compare it with traditional GS according to sensitivity at temporal and spatial scales and maximum concentrations of pesticide residues detected in-stream. Data collected from streams receiving water from three nested watersheds located in northwestern New Brunswick, Canada, were used in this study. The results showed that the hydrograph-based sampling method detected 20 to 30 % more pesticide cases than GS for rainfall events causing runoff. Grab sampling significantly underestimated average concentrations of pesticide residues by 50 % and maximum concentrations by 1 to 3 orders of magnitude. Using a modified sampler design, the spatial and temporal variability of pesticide residues was more accurately captured by hydrograph-based sampling, and therefore its use in monitoring programs is recommended.

A forest nutrient cycling and biomass model (ForNBM) based on year-round, monthly weather conditions, part I: assumption, structure and processing

A forest nutrient cycling and biomass growth model was developed to simulate nutrient cycling and NPP based on site and monthly mean weather conditions. A modular design was used to partition northern forest ecosystems into separate modules that address the following ecological variables: (1) hydrologic processes and temperatures to estimate moisture, percolation and temperature in forest floor, soil and subsoil; (2) soil acidity to estimate the H-ion balance in the soil, in the context of atmospheric deposition, nutrient uptake, weathering, and soil-ion retention; (3) cycling of N, S, Ca, Mg, and K to estimate nutrient uptake, mineralization, nitrification, immobilization, mineral soil weathering, nutrient exchange between soil exchange sites and solution, and nutrient leaching associated with atmospheric deposition; and (4) biomass to estimate NPP and its allocation to foliage, wood, and root, as well as litterfall and decomposition. After the model calibration, verification and validation, the model can be applied (1) to predict the rate of sustainable nutrient harvesting based on nutrient geochemical balance; (2) to determine limiting nutrients for forest growth; (3) to evaluate the effects of atmospheric acidic deposition on soil chemistry and growth; and (4) to evaluate the effects of forest harvesting on environmental issues, such as stream water quality.