scholarly journals Prediction of soil nutrient regime based on a model of DEM-generated clay content for the province of Nova Scotia, Canada

2013 ◽  
Vol 93 (2) ◽  
pp. 193-203 ◽  
Author(s):  
Zhengyong Zhao ◽  
M. Irfan Ashraf ◽  
Kevin S. Keys ◽  
Fan-Rui Meng
Keyword(s):  
Nova Scotia ◽  
High Resolution ◽  
Soil Nutrient ◽  
Clay Content ◽  
Parent Material ◽  
Forest Growth ◽  
Soil Maps ◽  
Large Area ◽  
Nutrient Regime ◽  
Coarse Resolution

Zhao, Z., Ashraf, M. I., Keys, K. S. and Meng, F-R. 2013. Prediction of soil nutrient regime based on a model of DEM-generated clay content for the province of Nova Scotia, Canada. Can. J. Soil Sci. 93: 193–203. Soil nutrient regime (SNR) maps are widely required by ecological studies as well as forest growth and yield assessment. Traditionally, SNR is assessed in the field using vegetation indicators, topography and soil properties. However, field assessments are expensive, time consuming and not suitable for producing high-resolution SNR maps over a large area. The objective of this research was to develop a new model for producing high-resolution SNR maps over a large area (in this case, the province of Nova Scotia). The model used 10-m resolution clay content maps generated from digital elevation model data to capture local SNR variability (associated with topography) along with coarse-resolution soil maps to capture regional SNR variability (associated with differences in landform/parent material types). Field data from 1385 forest plots were used to calibrate the model and another 125 independent plots were used for model validation. Results showed field-identified SNRs were positively correlated with predicted clay content, with some variability associated with different landform/parent material types. Accuracy assessment showed that 63.7% of model-predicted SNRs were the same as field assessment, with 96.5% within ±1 class compared with field-identified SNRs. The predicted high-resolution SNR map was also able to capture the influence of topography on SNR which was not possible when predicting SNR from coarse-resolution soil maps alone.

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

Land ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 449
Author(s):  
Zhengyong Zhao ◽  
Qi Yang ◽  
Xiaogang Ding ◽  
Zisheng Xing
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Soil Nutrient ◽  
Moisture Regime ◽  
Soil Maps ◽  
Error Matrix ◽  
Coarse Resolution ◽  
Soil Moisture Regime ◽  
Field Samples ◽  
Artificial Neural Network Ann

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

2021 ◽  
pp. 1-16
Author(s):  
Zhengyong Zhao ◽  
Qi Yang ◽  
Xiaogang Ding ◽  
Zisheng Xing
Keyword(s):  
High Resolution ◽  
Forest Soil ◽  
Digital Elevation Model ◽  
Cross Validation ◽  
Large Area ◽  
Coarse Resolution ◽  
Digital Elevation ◽  
Ann Models ◽  
Elevation Model ◽  
Fold Cross Validation

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.

Model prediction of soil drainage classes based on digital elevation model parameters and soil attributes from coarse resolution soil maps

2008 ◽  
Vol 88 (5) ◽  
pp. 787-799 ◽  
Author(s):  
Z. Zhao ◽  
T L Chow ◽  
Q. Yang ◽  
H W Rees ◽  
G. Benoy ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
High Resolution ◽  
Ann Model ◽  
Soil Maps ◽  
Soil Drainage ◽  
Coarse Resolution ◽  
Digital Elevation ◽  
Field Assessment ◽  
Elevation Model

High-resolution soil drainage maps are important for crop production planning, forest management, and environmental assessment. Existing soil classification maps tend to only have information about the dominant soil drainage conditions and they are inadequate for precision forestry and agriculture planning purposes. The objective of this research was to develop an artificial neural network (ANN) model for producing soil drainage classification maps at high resolution. Soil profile data extracted from coarse resolution soil maps (1:1 000 000 scale) and topographic and hydrological variables derived from digital elevation model (DEM) data (1:35 000 scale) were considered as candidates for inputs. A high-resolution soil drainage map (1:10 000) of the Black Brook Watershed (BBW) in northwestern New Brunswick (NB), Canada, was used to train and validate the ANN model. Results indicated that the best ANN model included average soil drainage classes, average soil sand content, vertical slope position (VSP), sediment delivery ratio (SDR) and slope steepness as inputs. It was found that 52% of model-predicted drainage classes were exactly the same as field assessment observations and 94% of model-predicted drainage classes were within ±1 class. In comparison, only 12% of maps indicated drainage classes were the same as field assessment observations based on coarse resolution soil maps and only 55% of points were within ±1 class of field assessed drainage classes. Results indicated that the model could be used to produce high-resolution soil drainage maps at relatively low cost. Key words: Soil drainage, artificial neural network model, ANN model, high-resolution soil maps, DEM, hydrology model

scholarly journals Prediction of Regional Forest Soil Nutrients Based on Gaofen-1 Remote Sensing Data

Forests ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1430
Author(s):  
Yingying Li ◽  
Zhengyong Zhao ◽  
Sunwei Wei ◽  
Dongxiao Sun ◽  
Qi Yang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Distribution ◽  
Forest Soil ◽  
Soil Nutrients ◽  
Predictive Power ◽  
Remote Sensing Data ◽  
Soil Nutrient ◽  
Soil Maps ◽  
Soil Layers ◽  
Coarse Resolution

The study on the spatial distribution of forest soil nutrients is important not only as a reference for understanding the factors affecting soil variability, but also for the rational use of soil resources and the establishment of a virtuous cycle of forest ecosystems. The rapid development of remote sensing satellites provides an excellent opportunity to improve the accuracy of forest soil prediction models. This study aimed to explore the utility of the Gaofen-1 (GF-1) satellite in the forest soil mapping model in Luoding City, Yunfu City, Guangdong Province, Southeast China. We used 1000 m resolution coarse-resolution soil map to represent the overall regional soil nutrient status, 12.5 m resolution terrain-hydrology variables to reflect the detailed spatial distribution of soil nutrients, and 8 m resolution remote sensing variables to reflect the surface vegetation status to build terrain-hydrology artificial neural network (ANN) models and full variable ANNs, respectively. The prediction objects were alkali-hydro-nitrogen (AN), available phosphorus (AP), available potassium (AK), and organic matter (OM) at five soil depths (0–20, 20–40, 40–60, 60–80, and 80–100 cm). The results showed that the full-variable ANN accuracy at five soil depths was better than the terrain-hydrology ANNs, indicating that remote sensing variables reflecting vegetation status can improve the prediction of forest soil nutrients. The remote sensing variables had different effectiveness for different soil nutrients and different depths. In upper soil layers (0–20 and 20–40 cm), remote sensing variables were more useful for AN, AP, and OM, and were between 10%–14% (R2), and less effective for AK at only 8% and 6% (R2). In deep soil layers (40–60, 60–80, and 80–100 cm), the improvement of all soil nutrient models was not significant, between 3 and 6% (R2). RMSE and ROA ± 5% also decreased with the depth of soil. Remote sensing ANNs (coarse resolution soil maps + remote sensing variables) further demonstrated that the predictive power of remote sensing data decreases with soil depth. Compared to terrain-hydrological variables, remote sensing variables perform better at 0–20 cm, but the predictive power decreased rapidly with depth. In conclusion, the results of the study showed that the integration of remote sensing with coarse-resolution soil maps and terrain-hydrology variables could strongly improve upper forest soil (0–40 cm) nutrients prediction and NDVI, green band, and forest types were the best remote sensing predictors. In addition, the study area is rich in AN and OM, while AP and AK are scarce. Therefore, to improve forest health, attention should be paid to monitoring and managing AN, AP, AK, and OM levels.

scholarly journals Estimating trembling aspen productivity in the boreal transition ecoregion of Saskatchewan using site and soil variables

2011 ◽  
Vol 91 (4) ◽  
pp. 661-669 ◽  
Author(s):  
Bradley Pinno ◽  
Nicolas Bélanger
Keyword(s):  
Soil Texture ◽  
Soil Nutrient ◽  
Clay Content ◽  
Parent Material ◽  
Site Quality ◽  
Categorical Variables ◽  
Continuous Variables ◽  
Trembling Aspen ◽  
Soil Variables ◽  
Soil Measurements

Pinno, B. D. and Bélanger, N. 2011. Estimating trembling aspen productivity in the boreal transition ecoregion of Saskatchewan using site and soil variables. Can. J. Soil Sci. 91: 661–669. The productivity of trembling aspen, as expressed by site quality index (SQI), in natural stands growing on three different soil parent material types (fluvial, lacustrine and glacial till) in the boreal transition ecoregion of Saskatchewan was evaluated by using soil and site variables. The soil and site variables used were either general categorical variables, such as parent material and ecosite, or continuous variables, such as soil texture (percent sand or clay), pH, carbon, nitrogen, C:N ratios, and elemental composition. It was not possible to reliably estimate SQI using only categorical site variables or continuous soil variables when all plots were grouped together. However, when plots were grouped by parent material type, over 45% of the variability in trembling aspen productivity was explained using the common soil measurements of texture and pH. In estimating SQI, there was an interaction between both pH and soil texture with parent material. On fluvial and lacustrine parent materials, increased clay content was positively correlated with SQI, but was negatively correlated with SQI on till, while pH was positively correlated with SQI on fluvial parent material, but negatively on lacustrine. Including more sophisticated measures of soil nutrient availability in the forest floor and BC horizons did not improve the SQI prediction. This study indicates that it is possible to estimate trembling aspen productivity using simple site and soil variables, provided that differences in soil properties within parent material groupings are considered in the analysis.

A soil nutrient regime index for forest practitioner decisions in Hesse, Germany: spatial explicit modelling of soil chemistry and integration by fuzzy-logic

2020 ◽  
Author(s):  
Felix Heitkamp ◽  
Bernd Ahrends ◽  
Jan Evers ◽  
Henning Meesenburg
Keyword(s):  
Fuzzy Logic ◽  
Soil Nutrient ◽  
Parent Material ◽  
Additive Models ◽  
Ground Vegetation ◽  
Organic Layer ◽  
Forest Site ◽  
Nutrient Regime ◽  
Soil Map ◽  
Informed Decisions

<p>Forests face considerable pressure from climate change, while demand of provided ecosystem services is high. Managing and planting forests need well informed decisions by practitioners, to fulfill the goal of sustainability. In Germany, informed decisions are derived from forest site evaluation maps, integrating biogeoecolocigal conditions (climate, soil water, nutrients). Here, we focus on mapping of nutrients in the federal state Hesse, Germany. For Hesse, a forest site map exists, which indicates a soil nutrient regime (SNR) index (classes very poor, poor, medium, rich, very rich). Site mapping was done in the field by experts, considering ground vegetation and soil morphology. Guidelines exist for choosing management options (i.e. suitable species composition, harvest restrictions, etc.), but if spatial information is not accurate, management decisions will be misguided.</p><p>Three major challenges regarding the currently available site information exist: (1) the spatial proportion of “medium” sites is exceptionally high (65% of mapped forest area) and while there is differentiation between parent materials, topography is neglected. (2) Whereas 80% of Hesse’s forests were mapped, there is need to fill the gaps. (3) The existing SNR index does not take analytical measurements of soil nutrients into account. Objectives were (1) to refine and expand the existing map of SNR by (2) including soil chemical properties from the second National Forest Soil Inventory (NFSI), (3) which have to be regionalised beforehand.</p><p>Stocks of Ca, Mg and K, base saturation, effective cation exchange capacity (90cm depth and organic layer), and C/N ratio (organic layer or 0-5 cm) of 380 profiles from the NFSI were chosen to characterise the SNR. Regionalisation was performed with generalised additive models (GAM) by using environmental relationships of the target variables with variables of climate, vegetation, parent material and soil properties (soil map 1:50,000). Ten-fold cross validation revealed R² values from 0.54 to 0.79, with low relative root mean square deviation (5 to 17%) and slopes not significantly different from 1. From the six successfully modelled target variables, we inferred a single SNR for each soil map polygon. This was challenging, because variables provided contrasting information regarding the SNR. We addressed this by using the Soil Inference Engine (SIE), which bases on fuzzy logic. Each variable received an optimality value for each SNR class. Using an expert-driven weighting system a SNR membership was inferred, whereas highest membership defined the SNR class. The result was highly sensitive towards parent material and topography. For instance, acidic parent material had lower SNR classes compared to base rich parent material. Within a given parent material, ridges where judged less nutrient rich compared to planes and topographic positions, where material is accumulated.</p><p>The results provide a much more differentiated and complete map for SNR, which mirror actual expectations of nutrient distribution across Hesse’s landscape units. The approach is transparent and inter-subjectively reproducible. The new map will be used to guide reforestation activities in Hesse after the severe forest disturbances by recent climatic extremes (e.g. drought, storms) and the approach can be transferred to other regions.</p>

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

2010 ◽  
Vol 90 (1) ◽  
pp. 75-87 ◽  
Author(s):  
Z. Zhao ◽  
Q. Yang ◽  
G. Benoy ◽  
T L Chow ◽  
Z. Xing ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
High Resolution ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Correlation Coefficient ◽  
Slope Position ◽  
Soil Maps ◽  
Coarse Resolution ◽  
Artificial Neural

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 significant influence on SOC distributions. The prediction of the selected two-input-node SOC model (VSP and coarse resolution soil map recorded SOC as inputs) had a correlation coefficient of 0.92 with measured values, and model predicted SOC values had 47.9% of the total points within ±0.5% of the measured values and 70.6% within ±1% of the measured values. The prediction od the selected four-input-node model (VSP, slope steepness, PSR and coarse resolution SOC values as inputs) had a correlation coefficient of 0.98 with measured values and model predicted SOC values had 75% of the total points within ±0.5% of the measured values and 87% within ±1% of the measured values. The prediction of the five-input-nodes model with soil drainage as additional input had a correlation coefficient of 0.99 with measured values, and model predicted SOC values had 87% of the total points within ±0.5% of the measured values and 98% of the total points within ±1% of the measured values. The calibrated SOC prediction model was used to produce a high-resolution SOC map for the Black Brook Watershed and the resulting SOC distribution map is considered to be realistic. Results indicated that DEM-derived hydrological parameters together with widely available coarse resolution soil map data could be used to produce high-resolution SOC maps with the ANN method.Key words: Soil organic carbon, artificial neural network model, high-resolution soil maps, digital elevation model, vertical slope position

Study of Tip-Surface Interactions in Scanning Tunneling Microscopy (STM) by Reflection Electron Microscopy (REM)

1990 ◽  
Vol 48 (1) ◽  
pp. 320-321
Author(s):  
W. Lo ◽  
J.C.H. Spence ◽  
M. Kuwabara
Keyword(s):  
High Resolution ◽  
Elastic Strain ◽  
Tunneling Current ◽  
Surface Damage ◽  
Surface Interactions ◽  
Graphite Surface ◽  
Scanning Tunneling ◽  
Large Area ◽  
Tunneling Microscopy ◽  
High Resolution Tem

Work on the integration of STM with REM has demonstrated the usefulness of this combination. The STM has been designed to replace the side entry holder of a commercial Philips 400T TEM. It allows simultaneous REM imaging of the tip/sample region of the STM (see fig. 1). The REM technique offers nigh sensitivity to strain (<10−4) through diffraction contrast and high resolution (<lnm) along the unforeshortened direction. It is an ideal technique to use for studying tip/surface interactions in STM.The elastic strain associated with tunnelling was first imaged on cleaved, highly doped (S doped, 5 × 1018cm-3) InP(110). The tip and surface damage observed provided strong evidence that the strain was caused by tip/surface contact, most likely through an insulating adsorbate layer. This is consistent with the picture that tunnelling in air, liquid or ordinary vacuum (such as in a TEM) occurs through a layer of contamination. The tip, under servo control, must compress the insulating contamination layer in order to get close enough to the sample to tunnel. The contaminant thereby transmits the stress to the sample. Elastic strain while tunnelling from graphite has been detected by others, but never directly imaged before. Recent results using the STM/REM combination has yielded the first direct evidence of strain while tunnelling from graphite. Figure 2 shows a graphite surface elastically strained by the STM tip while tunnelling (It=3nA, Vtip=−20mV). Video images of other graphite surfaces show a reversible strain feature following the tip as it is scanned. The elastic strain field is sometimes seen to extend hundreds of nanometers from the tip. Also commonly observed while tunnelling from graphite is an increase in the RHEED intensity of the scanned region (see fig.3). Debris is seen on the tip and along the left edges of the brightened scan region of figure 4, suggesting that tip abrasion of the surface has occurred. High resolution TEM images of other tips show what appear to be attached graphite flakes. The removal of contamination, possibly along with the top few layers of graphite, seems a likely explanation for the observed increase in RHEED reflectivity. These results are not inconsistent with the “sliding planes” model of tunnelling on graphite“. Here, it was proposed that the force due to the tunnelling probe acts over a large area, causing shear of the graphite planes when the tip is scanned. The tunneling current is then modulated as the planes of graphite slide in and out of registry. The possiblity of true vacuum tunnelling from the cleaned graphite surface has not been ruled out. STM work function measurements are needed to test this.

High Resolution Displacement of Functional Groups onto Fluorocarbon Resin Surface by Using ArF Excimer Laser

1996 ◽  
Vol 451 ◽  
Author(s):  
T. Shimizu ◽  
M. Murahara
Keyword(s):  
Contact Angle ◽  
High Resolution ◽  
Laser Beam ◽  
Thin Layer ◽  
Functional Groups ◽  
Excimer Laser ◽  
Laser Fluence ◽  
Large Area ◽  
Three Solutions ◽  
Arf Excimer Laser

ABSTRACTA Fluorocarbon resin surface was selectively modified by irradiation with a ArF laser beam through a thin layer of NaAlO2, B(OH)3, or H2O solution to give a hydrophilic property. As a result, with low fluence, the surface was most effectively modified with the NaAlO2 solution among the three solutions. However, the contact angle in this case changed by 10 degrees as the fluence changed only 1mJ/cm2. When modifying a large area of the surface, high resolution displacement could not be achieved because the laser beam was not uniform in displacing functional groups. Thus, the laser fluence was successfully made uniform by homogenizing the laser beam; the functional groups were replaced on the fluorocarbon resin surface with high resolution, which was successfully modified to be hydrophilic by distributing the laser fluence uniformly.

Mapping and Monitoring of the Land Use/Cover Changes in the Wider Area of ltanos, Crete, Using Very High Resolution EO Imagery With Specific Interest in Archaeological Sites

2019 ◽  
Author(s):  
Sawyer Reid stippa ◽  
George Petropoulos ◽  
Leonidas Toulios ◽  
Prashant K. Srivastava
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
Satellite Images ◽  
Archaeological Site ◽  
Digital Photography ◽  
Archaeological Sites ◽  
Large Area ◽  
High Resolution Imagery ◽  
Site Mapping ◽  
Rule Sets

Archaeological site mapping is important for both understanding the history as well as protecting them from excavation during the developmental activities. As archaeological sites generally spread over a large area, use of high spatial resolution remote sensing imagery is becoming increasingly applicable in the world. The main objective of this study was to map the land cover of the Itanos area of Crete and of its changes, with specific focus on the detection of the landscape’s archaeological features. Six satellite images were acquired from the Pleiades and WorldView-2 satellites over a period of 3 years. In addition, digital photography of two known archaeological sites was used for validation. An Object Based Image Analysis (OBIA) classification was subsequently developed using the five acquired satellite images. Two rule-sets were created, one using the standard four bands which both satellites have and another for the two WorldView-2 images their four extra bands included. Validation of the thematic maps produced from the classification scenarios confirmed a difference in accuracy amongst the five images. Comparing the results of a 4-band rule-set versus the 8-band showed a slight increase in classification accuracy using extra bands. The resultant classifications showed a good level of accuracy exceeding 70%. Yet, separating the archaeological sites from the open spaces with little or no vegetation proved challenging. This was mainly due to the high spectral similarity between rocks and the archaeological ruins. The satellite data spatial resolution allowed for the accuracy in defining larger archaeological sites, but still was a difficulty in distinguishing smaller areas of interest. The digital photography data provided a very good 3D representation for the archaeological sites, assisting as well in validating the satellite-derived classification maps. All in all, our study provided further evidence that use of high resolution imagery may allow for archaeological sites to be located, but only where they are of a suitable size archaeological features.

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