Relative error transmission and detection in Strategic Forest Management Model

The relative error (term error in the thesis always stands for relative error) transmission from Forest Resource Inventory (FRI) data to Strategic Forest Management Model (SFMM) outputs based on different FRI data to SFMM outputs bases on FRI survey factors such as age, stocking, height, and their combination, and species was studied. A basic input file from the Fort William Forest Management Unit was used to produce different experimental data sets which were entered into SFMMTOOL kit to generate SFMM input files. Each experimental data set was produced through modifying the basic data to make a given error rate inherent within. Through running SFMM input files of the experimental data sets, various SFMM outputs inherent error were produced, and were compared using statistical analysis technology and other analysis. It was concluded that FRI data errors such as the errors of species, age, stock, and combined errors of them could be transformed into SFMM outputs at different rates depending on the different survey factors. The results from the study indicated that species errors caused large and various SFMM output errors, depending on the original forest conditions. Age errors could cause small SFMM output errors except for the case with the age error of more than 15%. Stock errors can be transmitted into SFMM outputs at the same rate as the stock error value. Combination error can be transmitted to SFMM outputs at the same rate as the combination errors, but with a sharp increase of the rate when the combination error surpassed 20%. Age had an additive effect and interacting effect on the SFMM output errors when the combination error was equal to or greater than 20%. Based on the study, some suggestions to deal with the problems associated with FRI and SFMM application were made

Field Performance of Nine Soil Water Content Sensors on a Sandy Loam Soil in New Brunswick, Maritime Region, Canada

An in situ field test on nine commonly-used soil water sensors was carried out in a sandy loam soil located in the Potato Research Center, Fredericton, NB (Canada) using the gravimetric method as a reference. The results showed that among the tested sensors, regardless of installation depths and soil water regimes, CS615, Trase, and Troxler performed the best with the factory calibrations, with a relative root mean square error (RRMSE) of 15.78, 16.93, and 17.65%, and a r2 of 0.75, 0.77, and 0.65, respectively. TRIME, Moisture Point (MP917), and Gopher performed slightly worse with the factory calibrations, with a RRMSE of 45.76, 26.57, and 20.41%, and a r2 of 0.65, 0.72, and 0.78, respectively, while the Gypsum, WaterMark, and Netafim showed a frequent need for calibration in the application in this region

Validating Evapotranspiraiton Equations Using Bowen Ratio in New Brunswick, Maritime, Canada

Three methods including the Penman-Monteith (PM), Priestley-Taylor (PT), and 1963 Penman equation (PE) for calculating daily reference evapotranspiration (ETo) were evaluated in the Maritime region of Canada with the data collected from 2004 to 2007. An automatically operated meteorological station located on the Potato Research Centre, Agriculture and Agri-Food Canada, Fredericton, New Brunswick, Canada, was used to collect required meteorological data for evapotranspiration modeling. A Bowen Ratio system (BR) was setup near the Environment Canada grade one weather station to provide evapotranspiration observations for the validation research of reference evapotranspiration models. The results showed that the prediction from each of the tested models had a certain degree of offset in comparison with the observations obtained by the BR method. All of the tested models slightly overestimated evapotranspiration compared to the BR system by 5-14%, depending on the method. However, the PM generated a better fit to the pooled dataset while the PT produced the best prediction for the 2007 validation dataset. The PM generated the best estimation of evapotranspiration for year 2004 during a inter-annual comparison. The BR revealed that the average daytime ET for the site was around 2.5 mm day-1(±0.1) averaged for Julian day 157-276 in 2004 to 2006 and possible condensation was 0.16 mm day-1 for the same period. Crop coefficient (Kc) varied with different models, for example, 0.42 for the PM, 0.44 for the PT, and 0.67 for the PE with a slight yearly variation. With this set of Kc values, a validation with additional dataset collected in 2007 indicated that all three equations achieved a good fit with observations using the above Kc values. The PT performed slightly better than the other two models. A single factor analysis did not show any statistically significant difference between predicted and measured ET. With a consideration of simplicity and application for scaling up to landscape, this research suggested that the PT is the preferable method for estimating ET values in this region

The application of satellite-based model and bi-stable ecosystem balance concept to monitor desertification in arid lands, a case study of Sinai Peninsula

Desertification is responsible for depleting natural resources in arid lands, which is globally happening in an alarming rate. It is considered as the major environmental threat that affects about 40% of the world dry lands, which are populated by approximately one billion humans. In this paper the main objective is to discuss the recent and past research in monitoring and assessing desertification and land degradation using remote sensing technology and data. Recently, the Bi-stable ecosystem balance becomes a promising framework theory to detect the spatial extent of prime land degradation in arid and semi-arid environments; it is also a potential methodology to recognize the difference between the natural variability and instantaneous/ non-instantaneous desertification symptoms in dry lands. The satellite-based models are the future tools for monitoring very precisely desertification development, MODerate resolution Imaging Spectroradiometer (MODIS) satellite images are becoming the best datasets for building regional desertification monitoring algorithms because of its temporal scales (8-16 days). The MODIS Based Disturbance Index (MBDI) algorithm provide accurate information at six different study sites in Sinai Peninsula and showed the seasonal and yearly changes. This application can be used for monitoring decades of desertification development in North Africa, South Europe, and the Middle East, and can be linked to several fields such as agriculture sustainability, environmental conservations, rural development, and economics

Model Prediction of the Soil Moisture Regime and Soil Nutrient Regime Based on DEM-Derived Topo-Hydrologic Variables for Mapping Ecosites

Ecosites are required for stand-level forest management and can be determined within a two-dimensional edatopic grid with soil nutrient regimes (SNRs) and soil moisture regimes (SMRs) as coordinates. A new modeling method is introduced in this study to map high-resolution SNR and SMR and then to design ecosites in Nova Scotia, Canada. Using coarse-resolution soil maps and nine topo-hydrologic variables derived from high-resolution digital elevation model (DEM) data as model inputs, 511 artificial neural network (ANN) models were developed by a 10-fold cross-validation with 1507 field samples to estimate 10 m resolution SNR and SMR maps. The results showed that the optimal models for mapping SNR and SMR engaged eight and seven topo-hydrologic variables, together with three coarse-resolution soil maps, as model inputs, respectively; 82% of model-estimated SNRs were identical to field assessments, while this value was 61% for SMRs, and the produced ecosite maps had 67–68% correctness. According to the error matrix, the predicted SNR and SMR maps greatly alleviated poor prediction in the areas of extreme nutrient or moisture conditions (e.g., very poor or very rich, wet, or very dry). Thus, the new method for modeling high-resolution SNR and SMR could be used to produce ecosite maps in sites where accessibility is hard

Impacts of coarse-resolution soil maps and high-resolution digital-elevation-model-generated attributes on modelling forest soil zinc and copper

The depth-specific zinc (Zn) and copper (Cu) maps with high resolution (i.e., ≤10 m) are important for soil and forest management and conservation. The objective of this study was to assess the effects of easily accessible model inputs, i.e., existing coarse-resolution parent material, pH, and soil texture maps with 1:1 800 000–2 800 000 scale and nine digital elevation model (DEM)-generated terrain attributes with 10 m resolution, on modelling Zn and Cu distributions of forest soil over a large area (e.g., thousands of km2). A total of 511 artificial neural network (ANN) models for each depth (20 cm increments to 100 cm) were built and evaluated by a 10-fold cross-validation with 385 soil profiles from the Yunfu forest, South China, about 4915 km2 areas. The results indicated that the optimal models for five depths engaged five to seven DEM-generated attributes together with three coarse-resolution soil attributes as inputs, respectively, and accuracies for estimating Zn and Cu varied with R2 of 0.76–0.85 and relative overall accuracy ±10% of 74%–86%. The produced maps showed that DEM-generated sediment delivery ratio, topographic position index (TPI), and aspect were the most important attributes for predicting Cu, but flow length, TPI, and slope were for Zn, which heavily affected Zn and Cu distributions in detail. Boundaries of three coarse-resolution maps were still visible in the generated maps indicated that the maps affected the distributions of Zn and Cu in large scales. Thus, the modelling method, i.e., developing ANN models with k-fold cross-validation, can be used to map high-resolution Zn and Cu over a large area.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

Vegetation Degradation of Guanshan Grassland Suppresses the Microbial Biomass and Activity of Soil

Changes in vegetation influence the function of grassland ecosystems. A degradation of the vegetation type has been found from high to low altitudes in Guanshan grassland in the order of forest grassland (FG) < shrub grassland (SG) < herb grassland (HG). However, there is poor information regarding the effect of vegetation degradation on soil microbes in Guanshan grassland. Therefore, our study evaluated the impact of vegetation degradation on the microbial parameters of soil, as well as the mechanisms responsible for these variations. Soils were sampled from 0 to 30 cm under the FG, SG, and HG in Guanshan grassland for determining the microbial biomass, enzymatic activities, basal respiration (BR), and metabolic quotient (qCO2) in April and July 2017. The results showed that vegetation types are important factors that obviously influence the above-mentioned soil microbial properties. The FG and SG had significantly higher soil microbial biomass, enzymatic activities, and BR than those of the HG, but markedly lower qCO2 (p < 0.05). Soil pH, available nitrogen (AN), organic carbon (SOC), total phosphorus (TP), available P (AP), and total N (TN) were key factors in the decline in the soil microbial biomass and microbial activities of the degraded vegetation. Moreover, slope aspects also affected the soil microbial properties, with the east slope having higher soil microbial biomass, enzymatic activities, and BR and lower qCO2 than the west slope. Conclusively, vegetation degradation has led to a decline in the soil microbial biomass and microbial activities, indicating the degradation of the Guanshan grassland ecosystem

Comparison of Models for Spatial Distribution and Prediction of Cadmium in Subtropical Forest Soils, Guangdong, China

Cadmium (Cd) is a toxic metal and found in various soils, including forest soils. The great spatial heterogeneity in soil Cd makes it difficult to determine its distribution. Both traditional soil surveys and spatial modeling have been used to study the natural distribution of Cd. However, traditional methods are highly labor-intensive and expensive, while modeling is often encumbered by the need to select the proper predictors. In this study, based on intensive soil sampling (385 soil pits plus 64 verification soil pits) in subtropical forests in Yunfu, Guangdong, China, we examined the impacting factors and the possibility of combining existing soil information with digital elevation model (DEM)-derived variables to predict the Cd concentration at different soil depths along the landscape. A well-developed artificial neural network model (ANN), multi-variate analysis, and principal component analysis were used and compared using the same dataset. The results show that soil Cd concentration varied with soil depth and was affected by the top 0–20 cm soil properties, such as soil sand or clay content, and some DEM-related variables (e.g., slope and vertical slope position, varying with depth). The vertical variability in Cd content was found to be correlated with metal contents (e.g., Cu, Zn, Pb, Ni) and Cd contents in the layer immediately above. The selection of candidate predictors differed among different prediction models. The ANN models showed acceptable accuracy (around 30% of predictions have a relative error of less than 10%) and could be used to assess the large-scale Cd impact on environmental quality in the context of intensifying industrialization and climate change, particularly for ecosystem management in this region or other regions with similar conditions

A permittivity-conductivity joint model for hydrate saturation quantification in clayey sediments based on measurements of time domain reflectometry

Hydrate saturation (Sh) is one of the key parameters for resource assessment of hydrate reservoirs and production optimization of natural gas. There are still significant challenges in determining the Sh in clayey formations. Both dielectric and resistivity logging tools have been used for identifying and evaluating hydrate-bearing formations; however, there is little work on a joint analysis and modelling of the permittivity and resistivity for quantifying the Sh. To bridge the knowledge gap, we have proposed a novel permittivity-conductivity (P–C) joint approach based on TDR (time domain reflectometry)-derived parameters (i.e., apparent permittivity Ka and bulk conductivity σdc) in this work. The proposed P–C joint approach can provide a theoretical basis for the joint interpretation of dielectric and resistivity geophysical measurements on hydrate-bearing formations in the field. First, the basic theory for deriving the Ka and σdc from the TDR responses of hydrate-bearing sediments was formulated based on the dielectric polarization and electrical conduction mechanisms. Second, an experimental campaign was carried out including the development of experimental system, calibration of TDR probe and design of experimental scheme. Third, the influences of hydrate saturation, clay mineralogy and clay content on the TDR responses of unconsolidated sediments were examined. Then the Ka and σdc were related to Sh respectively, and finally a novel P–C joint model for the quantification of Sh in clayey sediments was established and verified. It has been demonstrated that: (1) the Ka of the clayey samples with hydrates decreases almost linearly with an increasing clay content up to 20 %, while the σdc of the smectite-bearing samples decreases nonlinearly in contrast to the linear trend for illite; (2) the power-law mixing formula incorporating an empirical exponent is a preferable permittivity model for hydrate-bearing clayey sediments due to its merits of empirical and theoretical nature, while the Simandoux equation is effective to account for the clay effects on the conductivity of hydrate-bearing sediments with smectite and illite; (3) the P–C joint model can be established by utilizing the porosity of hydrate-bearing sediments as a bridge parameter between Ka and σdc. The variation behavior of Ka and σdc with different types and contents of clay minerals can be explained by the difference of the amount of bound water and swelling effects between the illite-bearing and smectite-bearing samples. The proposed P–C joint model outperforms the standalone permittivity-based and conductivity-based models especially for the clayey cases. The root-mean-squared errors of the P–C joint models are 7.339, 2.930 and 2.065 % for the clean-sand samples, clayey samples with illite and smectite, respectively