Trend surface analysis of forest landscape pattern in Guandishan forest region of Shanxi,China

Landscape pattern is a widely used concept for the demonstration of landscape characteristic features. The integral spatial distribution trend of landscape elements is interested point in the landscape ecological research, especially in those of complex secondary forest regions with confusing mosaics of land cover. Trend surface analysis which used in community and population ecological researches was introduced to reveal the landscape pattern. A reasonable and reliable approach for application of trend surface analysis was provided in detail. As key steps of the approach, uniform grid point sampling method was developed. The efforts were also concentrated at an example of Guandishan forested landscape. Some basic rules of spatial distribution of landscape elements were exclaimed. These will be benefit to the further study in the area to enhance the forest sustainable management and landscape planning.

Limited Spatial Transferability of the Relationships Between Kriging Variance and Soil Sampling Spacing in Some Grasslands of Ireland:Implications for Sampling Design

Sampling plays an important role in acquiring precise soil information required in modern agricultural production worldwide, which determines both the cost and quality of final soil mapping products. For sampling design, it has been proposed possibile to transfer the relationships between kriging variance and sampling grid spacing from an area with existing information to other areas with similar soil-forming environments. However, this approach is challenged in practice because of two problems: i) different population vaxiograms among similar areas and ii) sampling errors in estimated variograms. This study evaluated the effects of these two problems on the transferability of the relationships between kriging variance and sampling grid spacing, by using spatial data simulated with three variograms and soil samples collected from four grasslands in Ireland with similar soil-forming environments. Results showed that the variograms suggested by different samples collected with the same grid spacing in the same or similar areas were different, leading to a range of mean kriging variance (MKV) for each grid spacing. With increasing grid spacing, the variation of MKV for a specific grid spacing increased and deviated more from the MKV generated using the population variograms. As a result, the spatial transferability of the relationships between kriging variance and grid spacing for sampling design was limited.

Effect of Soil Sampling Density on Detected Spatial Variability of Soil Organic Carbon in a Red Soil Region of China

Spatial variability of soil organic carbon (SOC) of different land use patterns and soil types was examined in a county-wide red soil region of South China,using six sampling densities,14,34,68,130,255,and 525 samples designed by the method of grid sampling in 6 different grid sizes,labeled as D14,D34,D68,D130,D255,and D525,respectively.The results showed that the coefficients of variation (CVs) of SOC decreased gradually from 62.8% to 47.4% with the increase in soil sampling densities.The SOC CVs in the paddy field change slightly from 30.8% to 28.7%,while those of the dry farmland and forest land decreased remarkably from 58.1% to 48.7% and from 99.3% to 64.4%,respectively.The SOC CVs of the paddy soil change slightly,while those of red soil decreased remarkably from 82.8% to 63.9%.About 604,500,and 353 (P < 0.05) samples would be needed a number of years later if the SOC change was supposedly 1.52 g kg-1,based on the CVs of SOC acquired from the present sampling densities of D14,D68,and D525,respectively.Moreover,based on the same SOC change and the present time CVs at D255,the ratio of samples needed for paddy field,dry farmland,and forest land should be 1:0.81:3.33,while the actual corresponding ratio in an equal interval grid sampling was 1:0.74:0.46.These indicated that the sampling density had important effect on the detection of SOC variability in the county-wide region,the equal interval grid sampling was not efficient enough,and the respective CV of each land use or soil type should be fully considered when determining the sampling number in the future.

Comparison of sampling designs for calibrating digital soil maps at multiple depths

Digital soil mapping (DSM) aims to produce detailed maps of soil properties or soil classes to improve agricultural management and soil quality assessment. Optimized sampling design can reduce the substantial costs and efforts associated with sampling, profile description, and laboratory analysis. The purpose of this study was to compare common sampling designs for DSM, including grid sampling (GS), grid random sampling (GRS), stratified random sampling (StRS), and conditioned Latin hypercube sampling (cLHS). In an agricultural field (11 ha) in Quebec, Canada, a total of unique 118 locations were selected using each of the four sampling designs (45 locations each), and additional 30 sample locations were selected as an independent testing dataset (evaluation dataset). Soil visible near-infrared (Vis-NIR) spectra were collected in situ at the 148 locations (1 m depth), and soil cores were collected from a subset of 32 locations and subdivided at 10-cm depth intervals, totaling 251 samples. The Cubist model was used to elucidate the relationship between Vis-NIR spectra and soil properties (soil organic matter (SOM) and clay), which was then used to predict the soil properties at all 148 sample locations. Digital maps of soil properties at multiple depths for the entire field (148 sample locations) were prepared using a quantile random forest model to obtain complete model maps (CM-maps). Soil properties were also mapped using the samples from each of the 45 locations for each sampling design to obtain sampling design maps (SD-maps). The SD-maps were evaluated using the independent testing dataset (30 sample locations), and the spatial distribution and model uncertainty of each SD-map were compared with those of the corresponding CM-map. The spatial and feature space coverage were compared across the four sampling designs. The results showed that GS resulted in the most even spatial coverage, cLHS resulted in the best coverage of the feature space, and GS and cLHS resulted in similar prediction accuracies and spatial distributions of soil properties. The SOM content was underestimated using GRS, with large errors at 0–50 cm depth, due to some values not being captured by this sampling design, whereas larger errors for the deeper soil layers were produced using StRS. Predictions of SOM and clay contents had higher accuracy for topsoil (0–30 cm) than for deep subsoil (60–100 cm). It was concluded that the soil sampling designs with either good spatial coverage or feature space coverage can provide good accuracy in 3D DSM, but their performances may be different for different soil properties.

Determination of optimal samples for robot calibration based on error similarity

Abstract Industrial robots are used for automatic drilling and riveting. The absolute position accuracy of an industrial robot is one of the key performance indexes in aircraft assembly, and can be improved through error compensation to meet aircraft assembly requirements. The achiev- able accuracy and the difficulty of accuracy compensation implementation are closely related to the choice of sampling points. Therefore, based on the error similarity error compensation method, a method for choosing sampling points on a uniform grid is proposed. A simulation is conducted to analyze the influence of the sample point locations on error compensation. In addition, the grid steps of the sampling points are optimized using a statistical analysis method. The method is used to generate grids and optimize the grid steps of a Kuka KR-210 robot. The experimental results show that the method for planning sampling data can be used to effectively optimize the sampling grid. After error compensation, the position accuracy of the robot meels the position accuracy require- ments.