CN100342380C - GPS sample strip ground collection method for growing portion of crop based on ridge-direction parallelism - Google Patents

Abstract

The present invention relates to a ground collecting method of GPS sample strips of crop planting parts based on parallel furrow direction, which belongs to the information technical field. On the basis of the characteristic of parallel distribution of the furrow direction of farmland crops, using the characteristic that the farmland crops present the parallel distribution of the ridge direction, by combining the GPS sample line data of the crop planting parts and the side-shaped space data of farmland plot, adding in the ridge direction information of the farmland crops, widening the ground collecting data of the GPS sample line of the crop planting parts in a line form to the GPS sample strip data of the crop planting parts in one side form, the quantity of the ground collecting data of the crop planting parts is consequently increased by a big margin, and the defect that using the ground collecting method of the GPS sample line of the crop planting parts to conclude the crop planting parts in sampling areas has low-rise sampling accuracy is overcome.

Description

The GPS Transect Ground Acquisition Method Based on the Parallel Ridge Direction of Crop Planting

technical field

The invention belongs to the field of information technology, and relates to a ground collection method for crop planting numbers, in particular to a crop planting number GPS transect ground collection method based on parallel ridge directions.

Background technique

The combination of ground acquisition methods and space remote sensing technology has great potential to obtain timely and accurate information on regional crop planting structure. In the past 30 years, there have been many ground collection methods for the number of cultivated land crops in the sample area, including the field sample mapping method based on aerial photos, the square sample field sample point observation method, the GPS positioning video acquisition (GVG) method, and GPS transect ground acquisition method, etc. The first two methods collect data with higher accuracy. However, the artificial ground observation and measurement methods make the work efficiency quite low; the latter two methods have greatly improved the ground acquisition efficiency due to the convenience of transportation, but the accuracy of the acquired data is not high.

The Chinese Patent Bulletin discloses a "Spatial-Temporal Positioning Field Feature Information Acquisition, Processing and Analysis Method" (public number CN1302033A, public date July 4, 2001), and its technical solution is mainly supported by a power inverter , using a certain configuration of notebook computers, digital cameras, GPS receivers, and manual input devices, through the software system developed by the geographic information system Titan3.1 control and Visual Basic6. processing and analysis. Among them, the GPS transect ground collection method only collects the crop planting data at the transects on both sides of the traffic line. The amount of sample information it obtains is very limited, which will directly affect the sampling accuracy of the crop planting rate.

Contents of the invention

In order to overcome the shortage of limited sample information obtained by the existing crop planting number GPS transect ground collection method, the present invention provides a ground collection method based on crop planting number GPS transects parallel to the ridge direction. Based on the parallel distribution characteristics, by introducing the spatial data of cultivated land plots and combining the crop planting number GPS transect data, the amount of information on the crop planting number obtained on the ground can be greatly improved, and the crop planting number GPS transect ground acquisition method can be improved. The lack of low data precision.

The technical scheme proposed by the present invention is divided into the following six basic steps, that is, collecting the linear spatial data of GPS transects, obtaining the planar spatial data of cultivated land plots, extracting the planar spatial data of ridge-oriented plots, and generating the planar space of crop plots Data, aggregation of GPS transect surface spatial data, and estimation of the number of crops planted in the sampling area, the specific content is as follows:

1) Collecting GPS transect linear space data: in the crop planting area, use the "temporal and spatial positioning field object information collection, processing and analysis method" cited in the background technology of this manual to record the sampling data driven by field vehicles. At the same time, enter the crop type code on the side of the road by pressing the button to generate GPS transect vector data with crop planting type information. Different types of GPS transect line segments correspond to different types of crops;

2) Acquisition of surface spatial data of cultivated land plots: use geographic information system (GIS) software tools to generate or update detailed survey spatial data of land in the sampling area (scale ≥ 1:10,000) through high spatial resolution remote sensing images, and generate cultivated land in the sampling area Parcel planar spatial data. The boundaries of cultivated land plots are determined by linear features such as roads, shelterbelts, rivers, and other types of planar features (such as woodlands, grasslands, etc.);

3) Extract the surface spatial data of ridge-oriented plots: with the help of GIS software tools, overlay the surface-shaped spatial data of cultivated land plots on high-resolution remote sensing images, identify the crop ridge orientation information in each cultivated land plot, and then The planar spatial data of the divided cultivated land plots generates the planar spatial data of the ridge-oriented plots with the same ridge direction;

4) Generating area-like spatial data of crop plots: With the help of GIS software tools, the spatial data of crop planting number GPS transects and the spatial data of ridge-wise plots are superimposed together, and the crop planting type changes of GPS transect vector data are The starting point is to make a parallel line to the crop ridge, divide the area data of the ridge area into the area data of the crop area with the same crop type, and assign the crop type code of the GPS transect to the crop area In the shape space data attribute table;

5) Aggregation of GPS transect surface spatial data: with the help of GIS software tools, the spatial data of the number of crop planting GPS transects and the spatial data of crop plots are superimposed together, based on the number of GPS transects of each crop planting, Select the corresponding crop plot data respectively and assign the crop planting number GPS transect codes to these crop plot spatial data attribute tables to form the crop planting number GPS transect spatial data;

6) Estimate the number of crops planted in the sampling area: with the help of two-dimensional data table statistical software tools, classify and count the area field in the area data attribute table of crop plots in the GPS transect according to the crop type code, and calculate the GPS transect According to the proportion of planting area of each crop type, the number of crops planted in the sampling area can be inferred according to the statistical calculation formula of the cluster sampling method.

The invention utilizes the characteristic of parallel distribution of ridge directions of cultivated land crops, and combines the GPS transect data of crop planting numbers with the planar space data of cultivated land plots, and adds the ridge direction information of cultivated land crops, so that the crops in the form of lines can be planted The ground collection data of several GPS transects is extended to the crop planting several GPS transect data in the form of area, thus greatly increasing the quantity of crop planting several ground collection data and overcoming the problem of using the crop planting several GPS transect ground The collection method infers the defect that the sampling accuracy of crop planting percentage in the sampling area is not high.

Description of drawings

Figure 1 is a schematic diagram of the spatial distribution of different types of crops in the farmland. In the figure 1 is the spatial distribution of a single crop, and 2 is the traffic route.

Figure 2 is a schematic diagram of arable land plots in farmland. Figure 3 is the cultivated land plot.

Figure 3 is a schematic diagram of ridge-oriented plots in farmland. 4 in the figure is a ridge-oriented plot.

Fig. 4 is a schematic diagram of crop planting number GPS transect line vector data. 5 in the figure is the GPS transect, and 10 is the pointing line of the cultivated land plot measured by the GPS transect of the number of crops planted.

Fig. 5 is a schematic diagram of the spatial distribution of crops obtained by the GPS transect ground collection method based on the number of crops planted in parallel to the ridge. Figure 7 is the GPS transect of crop planting percentage.

Fig. 6 is a schematic diagram of crop planting number GPS transect area vector data. Figure 6 is the crop plot.

Figure 7 is an aerial color image of a test area in Dehui City, Jilin Province.

Fig. 8 is the spatial distribution map of crops extracted from the aerial color image of the test area (Fig. 7). In the figure, areas of different colors represent different types of crop land plots, 1 is the spatial distribution of a single crop, and 8 is the area legend symbol.

Figure 9 is a plot of cultivated land in the test area. Figure 3 is the cultivated land plot.

Figure 10 is a plot map of the ridge direction in the test area. In the figure, 4 is the ridge direction plot, and 9 is the crop ridge direction line.

Figure 11 is the GPS transect data map of crop planting percentage in the test area. In the figure, different colors of GPS transects are used to represent different types of crops, 5 is the GPS transect, 11 is the linear legend symbol, and 10 is the pointing line of the cultivated land plot measured by the GPS transect of the number of crops planted.

Figure 12 is a GPS transect data map of the number of crops planted in the test area. Figure 6 is the crop plot.

Figure 13 is the spatial distribution map of crops obtained by GPS transect ground collection method based on the number of crops planted in the test area based on the parallel characteristics of crop ridges. In the figure, different color areas represent different types of crop plots, 7 is the GPS transect of crop planting numbers, and 8 is the area legend symbol.

Detailed ways

The object of the crop planting number GPS transect ground acquisition method based on the parallel feature of crop ridges in the present invention is to obtain as much accurate information as possible on the spatial distribution of crops in the sampling area (as shown in Figure 1) by means of ground acquisition, so as to accurately infer work The number of crops planted in the area.

The cultivated land block 3 spatial data (as shown in Figure 2) used in the implementation process of the present invention can be acquired and updated by means of ground surveying and mapping, or utilize space remote sensing technology, and use GIS software to process and manage; The spatial data is superimposed on the high spatial resolution remote sensing images of the same area, and the spatial data of the cultivated land block 3 are divided into the spatial data of the ridge-oriented block 4 with the same ridge direction by using GIS software editing tools (as shown in Figure 3); The number of crops planted in the sampling area and the GPS transect data (as shown in Figure 4) can be obtained in the sampling area by applying the "temporal and spatial positioning field object information collection, processing and analysis method" quoted in the background technology of this specification; Under the environment, on the basis of the spatial superposition of the spatial data of the ridge-oriented plot 4 in the sampling area and the data of the crop planting number GPS transect 5, each adjacent ridge-oriented plot was made from the change points of different crop types on the GPS transect. The ridge-wise parallel lines of 4 divide the ridge-wise plot 4 spatial data into the crop plot 6 spatial data with the same type of crops distributed, and assign the crop type codes of each line segment of the GPS transect 5 to the adjacent crop plot 6 space In the data attribute table record (as shown in Figure 5); under the GIS software environment, the crop plot 6 spatial data beside the GPS transect line 5 is aggregated to generate the crop planting GPS transect data ( As shown in Figure 6); use the electronic two-dimensional data table statistical software tool to count the planting area of different crop types and their proportions in the attribute table, and according to the statistical calculation formula of the cluster sampling method, each GPS transect The calculated crop planting ratio can be used to infer the crop planting ratio and its variation variance in the entire sampling area.

The above method based on the ridge-to-parallel crop planting into several GPS transect ground collection methods can be illustrated by the following examples:

In a certain area of Dehui City, Jilin Province, the ground collection experiment of several GPS transects based on crops planted in parallel to the ridge direction was carried out. The steps are as follows:

1) Obtain the high-spatial-resolution aerial remote sensing true-color image data (as shown in Figure 7) of a certain area in Dehui City, Jilin Province as the test area in combination with the "temporal and spatial positioning field object information collection, Processing and analysis method" The crop planting number GPS sample line 5 data collected on the field (as shown in Figure 11), using the View tool in the ArcView 3.3 software, through the human-computer interaction visual interpretation method, extract and generate the crops in the test area Spatial distribution map of planting types (as shown in Figure 8). Using the Table tool in the ArcView 3.3 software, the planting number data of 10 crops in the entire test area obtained by the remote sensing survey method were counted, as shown in the remote sensing survey data column of crop planting number measurement in Table 1.

2) Under the ArcView 3.3 software environment, with the aerial remote sensing true-color image as the background, according to the linear road information identified from the image, extract and generate the spatial data of the cultivated land plots in the test area (as shown in Figure 9), each cultivated land Plot 3 is divided by traffic lines 2 with a certain width.

3) Under the ArcView 3.3 software environment, again using the aerial remote sensing true-color image as the background, identify the crop distribution ridge direction information in the cultivated land plot 3 and generate the data of the crop ridge direction line 9, and then plot the cultivated land plot 3 space in the test area. The data is divided into 4 spatial data of ridge-oriented plots with the same ridge orientation (as shown in Figure 10).

4) The data collected on-the-spot by the "Space-Temporal Positioning Field Feature Information Collection, Processing and Analysis Method" cited in the background technology of this specification (as shown in Figure 11) includes the data on the side of the traffic line 2 crop type distribution information, the crop type distribution measured by the GPS transect 5 data is represented by the number of crops planted by the GPS transect to measure the cultivated land plot pointing line 10 to represent.

5) Under the ArcView 3.3 software environment, the spatial data of the ridge direction plot 4 with a unified spatial projection and the crop planting number GPS transect 5 data are superimposed, combined with the crop ridge direction line 9 and the crop distribution pointing line, Make the parallel line of crop ridge direction line 9 from the crop type change place of GPS transect 5, divide the spatial data of ridge direction plot 4 into crop plot 6 spatial data with the same crop type distribution, and divide each line segment of GPS transect 5 The crop type codes are assigned to the spatial data attribute table records of adjacent crop plots 6, and the spatial distribution of crop types in the test area is shown in Figure 12.

6) Under the ArcView 3.3 software environment, the spatial data of the ridge-wise plot 4 in the test area and the crop planting number GPS transect 5 data were superimposed, and based on the GPS transect 5 of each crop planting number, the other crops were selected respectively. The corresponding crop plots 6 and the number of crop planting GPS transect codes are assigned to these crop plots 6 spatial data attribute tables to form the crop planting number GPS transect spatial data (as shown in Figure 13).

7) Since the number of crops planted in the test area is fully measured by the GPS transect, the next step is to omit the statistics and inference of the number of crops planted by the cluster sampling of the GPS transect, and the result of the ground collection is the spatial data of the crop plot 6 in the test area Proportions of acreage for different crop types in the attribute table. The statistics of the number of crops is completed in the ArcView 3.3 software environment, using the Table tool to operate the spatial data attribute table of the crop plot 6 in the experimental area. The results are shown in the GPS transect data of the measurement of the number of crops in Table 1 column, and the GPS sample line 5 data column represents the crop planting number GPS sample line ground collection method in the patent "time-space positioning field feature information collection, processing and analysis method" cited in the background technology of this manual. the result of.

The measurement absolute error and relative error data columns in Table 1 represent the GPS transects and GPS transects, which are calculated using the crop planting number data obtained from remote sensing surveys as the true value. The precision status of the data. As can be seen from this table, compared with the crop planting number GPS transect method, the crop planting number GPS transect ground acquisition method based on the crop ridge direction parallel feature proposed by the present invention, no matter from the absolute error or from the relative error , are much higher than the former, and the accuracy is nearly doubled.

Table 1 The number of crops and their accuracy comparison table obtained by various measurement methods in the test area type of crops Crop planting ratio measurement (%) Measurement absolute error (%) Measurement relative error (%) remote sensing survey GPS transect GPS sample line GPS transect GPS sample line GPS transect GPS sample line Chinese cabbage 2 3 3 1 1 0.02 0.02 sorghum twenty one 20 14 -1 -7 0.21 1.47 Millet 17 17 18 0 1 0.00 0.17 apple orchard 2 1 6 -1 4 0.02 0.08 vineyard 2 2 3 0 1 0.00 0.02 rice 20 twenty three twenty one 3 1 0.60 0.20 beet 11 11 11 0 0 0.00 0.00 Potato 7 6 9 -1 2 0.07 0.14 wheat 15 12 16 -3 1 0.45 0.15 corn 3 4 2 1 -1 0.03 0.03 total 100 100 100 0 0 1.40 2.28

Claims (2)

1. the crop-planting based on ridge-direction parallelism becomes number GPS belt transect ground collection method, it is characterized in that the following step:

1) gathers GPS line-transect wire spatial data: become the number sample area at crop-planting, utilize " the field ground feature information acquisition of space-time location, processing and analytical approach ", write down the sampling route that open-air vehicle travels, simultaneously by button input channel roadside side agrotype coding, generation has the GPS line-transect vector data of crop-planting type information, and dissimilar GPS line-transect line segment correspondences dissimilar crops;

2) obtain the planar spatial data in plot, arable land: by Geographic Information System GIS Software tool, generate or renewal sample area clay detailed Investigation spatial data, generate the planar spatial data in plot, sample area arable land by high spatial resolution remote sense image;

3) extract the ridge to the planar spatial data in plot: by the GIS Software tool, the planar spatial data in plot, arable land is covered on the high spatial resolution remote sense image, discern in each plot, arable land the crop ridge to information, cut apart the planar spatial data in plot, arable land then in view of the above, generate have identical ridge to the ridge to the planar spatial data in plot;

4) generate the planar spatial data in crop plot: by the GIS Software tool, become number GPS line-transect spatial data to be stacked and placed on to the plot spatial data crop-planting with the ridge, with variation place of GPS line-transect vector data crop-planting type is starting point, do the crop ridge to parallel lines, the ridge is divided into the planar spatial data in crop plot with identical agrotype once more to the planar spatial data in plot, and the agrotype of GPS line-transect coding is imparted in the planar spatial data attribute list in crop plot;

5) the planar spatial data of polymerization GPS belt transect: by the GIS Software tool, become number GPS line-transect spatial data to place crop-planting with crop field block space stacked data, plant into several GPS line-transects based on every row culture species, select its corresponding crop field blocks of data respectively and become number GPS line-transect coding to compose in these crop field block space data attribute lists crop-planting, form crop-planting and become number GPS belt transect spatial data;

6) calculate that the sample area crop-planting becomes number: by two-dimensional data table statistical software instrument, to the area field in the planar spatial data attribute list in GPS belt transect crop plot, carry out statistic of classification according to the agrotype coding, and calculate every kind of agrotype cultivated area ratio in the GPS belt transect, and then infer that according to the statistic computing formula of chester sampling mode the sample area crop-planting becomes number.

2. the crop-planting based on ridge-direction parallelism according to claim 1 becomes number GPS belt transect ground collection method, it is characterized in that obtaining the planar spatial data in plot, arable land, extract the ridge to the planar spatial data in plot, generate the planar spatial data in crop plot, the planar spatial data of polymerization GPS belt transect, reckoning sample area crop-planting and become several five steps to realize by ArcView 3.3 softwares.

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