CN110598514B - Method for monitoring plot scale crop seeding area of land reclamation project area - Google Patents

Abstract

The invention relates to a method for monitoring the plot scale crop seeding area of a land reclamation project area, and belongs to the technical field of land monitoring. The method comprises the steps of obtaining high spatial-temporal resolution NDVI of agricultural land parcels in a land reclamation project area by utilizing a high temporal resolution image and a high spatial resolution image and combining vector land utilization data; introducing RGB image spectral characteristics and textural characteristics of Google Earth, and extracting crop planting characteristic spaces of paddy fields, dry lands and irrigated paddy fields; secondly, extracting the land plot scale multiple planting level of the land improvement project area by adopting a secondary difference algorithm and a land plot statistical method; and finally, extracting the plot scale crop seeding area and the change characteristics of the plot scale crop seeding area in the land improvement project area according to a space statistical method. The invention has the advantages of low cost, convenient processing, difficult influence by subjective factors and objective and reliable result.

Description

A method for monitoring the sowing area of crops at the plot scale in land consolidation project areas

technical field

The invention relates to a method for monitoring the sowing area of crops at the plot scale in a land improvement project area, and belongs to the technical field of land monitoring.

Background technique

Land consolidation, one is to make the cultivated land concentrated and contiguous by bulldozing the uneven plots of cultivated land, so as to provide convenience for various agricultural machinery to carry out farming activities such as transplanting, harvesting, using pesticides and applying chemical fertilizers; the other is to repair dilapidated ditches Or build new irrigation and drainage facilities, etc., and then provide support for the realization of drought and flood protection; the third is to build field roads to facilitate the entry and exit of agricultural machinery and the daily care and management of farmers; the fourth is to build a field protection network and take relevant measures to prevent landslides and wind damage, etc. . The important goal of land consolidation is to improve the production conditions such as water distribution in the field, realize the convenience of management, and then change the quality of agricultural land, the level of multiple cropping (the number of crops planted in a certain agricultural land in a year), the area of crops, etc. land productivity and agricultural income levels.

Land consolidation is an important measure to improve the production capacity and stability of agricultural land (land used to grow crops), and an important means to build high-standard farmland. In recent years, it has become a key content of national strategic deployment. However, how the agricultural land production changes in the land consolidation project area needs to be identified quantitatively.

The sown area of crops is one of the important investigation contents of agricultural production changes in the land consolidation project area. The agricultural land plot is a small area of agricultural land formed by dividing the agricultural land in a certain area by other land or linear features. The production conditions such as water and soil are relatively close. The sown area of crops at the plot scale refers to the total area of crops that have been planted in each agricultural land plot in a year. Finding out the sown area of crops at the plot scale in the land consolidation project area is crucial for disaster response, fine management and protection of agricultural land, and zoning utilization and improvement. very necessary.

At present, at the practical level, the sowing area of crops on agricultural land is mainly carried out by sampling survey method; while at the research level, the sowing area of a specific crop is mainly extracted or extracted using low spatial resolution remote sensing images. However, the method of extracting sown area for specific crops is difficult to effectively monitor the sown area at the plot scale, because 1) for different pixels in the same plot in the first half (or second half) of the year, if a certain pixel When the planted crop a is in the period of turning green and jointing, while the crop b planted in another pixel is in the period of sowing and germination, that is, it is not in the same growth period, if only one period of image is selected for sowing in the first half (or second half) Area extraction will obviously miss a crop, and the image of which time phase to choose depends on the type of crop, but the type of crop is determined according to farming habits, and crop rotation and fallow may occur in land improvement project areas. It makes it difficult to carry out dynamic monitoring every year. 2) For the same pixel, if different crops are planted in the first half and the second half of the year, it is necessary to investigate the planted crop type and prior knowledge to select the phase every year, which will make monitoring difficult; The detailed grasp of phenological period and crop growth characteristics can only realize the monitoring of this specific crop. Therefore, for the same plot, it is difficult to effectively monitor the crop sowing area of the plot by selecting several time-phase monitored specific crop sowing areas for superposition.

In addition, remote sensing extraction methods with low spatial resolution cannot achieve plot-scale monitoring. The reason is that when a pixel covers the entire plot, it is impossible to describe the interior of the plot in detail, which is manifested in different areas within the plot. When different crops are planted, the value of the pixel is the comprehensive result of each crop. If the proportions of various crops planted in different areas in each plot are inconsistent, it is difficult to set the peak value judgment rule, and if there are always crops planted in the plot When it is difficult to effectively extract the sown area, for example, half of the area a-d is the wheat development period, and half of the area c-e is the rice development period, then it is difficult to form the required "increase-decrease" inverted V at the plot scale. type crop growth curve; if a pixel covers multiple plots, it is even more difficult to separate the sowing area between plots.

In summary, the existing technology has the following limitations: 1) The sampling survey method cannot reflect the heterogeneity of the global space, cannot obtain historical information, and the sample selection is easily affected by subjective factors, and the cost of time, manpower, and financial resources is high. 2) It is difficult to obtain the total area of all crops in a year by extracting the sown area of a specific crop, which is especially prominent in the case of intercropping of multiple crops and overlapping growing seasons in certain periods. 3) The method of extracting the sown area of crops from remote sensing images with low spatial resolution cannot be applied at the plot scale, because most of the agricultural land plots in my country are relatively small, especially in mountainous and hilly areas. Remote sensing images will make it difficult to segment the blocks, and the probability of misjudgment is extremely high; and if high-precision images are used to extract the sown area of crops, there are problems that are difficult to be widely used due to the high price, large amount of data, and very difficult processing. Such images are provided by recently launched satellites, but there are outstanding problems in the long-term dynamic monitoring, especially in the acquisition of historical information.

Contents of the invention

The technical problem to be solved by the present invention is to provide a method for monitoring the sowing area of crops at the plot scale in the land improvement project area, which is low in cost, convenient in processing and not easily affected by subjective factors.

In order to solve the above-mentioned technical problems, the technical solution proposed by the present invention is: a method for monitoring the sowing area of crops at the plot scale in a land consolidation project area, comprising the following steps:

Step 1. Obtain the high temporal resolution image, high spatial resolution image and vector land use data of the year to be monitored covering the land improvement project area, and the vector land use data includes the boundaries of cultivated land plots and land use types;

High temporal resolution NDVI and high spatial resolution NDVI are obtained from high temporal resolution images and high spatial resolution images respectively, and high temporal resolution NDVI and high spatial resolution NDVI are fused by ESTARFM method to obtain high temporal and spatial resolution NDVI ;

Step 2. Using the boundary of the cultivated land as a constraint condition, segment the high spatial-temporal resolution NDVI to obtain the high temporal-spatial resolution image NDVI of the agricultural land in the land improvement project area;

According to the boundary of the cultivated land, combined with the vegetation planting area identified by the Google Earth high-resolution image based on the spectrum and texture features, the typical crop planting area in the land consolidation project area is obtained;

According to the land use type, the typical crop planting area is divided into three types: paddy field, dry land and irrigated land, and the high temporal and spatial resolution NDVI time series features of these three types of typical crop planting areas are respectively extracted. The high temporal and spatial resolution NDVI Timing features include curve shape, curve peak and time distance between peaks formed by high spatio-temporal resolution NDVI time series data;

Step 3. According to the high-temporal-spatial-resolution NDVI time-series characteristics of the typical areas where three types of crops are planted, combine the quadratic difference algorithm to extract the peak value of each pixel in the high-spatial-temporal-resolution NDVI time-series characteristics, according to the peak size and the distance between the peaks Set the preset peak screening rules, and the number of filtered peaks is the multiple cropping level of the pixel;

Step 4. Combining the boundary of the cultivated land plot in the vector land use data, the multiple cropping level of the pixel is counted by plot, and the average multiple cropping level of each cultivated land plot is calculated by using the area ratio analysis method;

Step 5: Estimate the crop sown area of each cultivated land plot by the product of the area of each cultivated land plot and the average multiple cropping level, and calculate the crop sown area of the land consolidation project area by means of spatial statistical summation.

Step 6. Use the inter-annual difference and spatial autocorrelation analysis methods to obtain the temporal and spatial changes of the crop sowing area in the land consolidation project area on the plot scale, separate and output the high-value aggregation area and the low-value aggregation area, and the high-value The value agglomeration area represents the area where the crop sown area is concentrated and the low value agglomeration area represents the area where the crop sown area is concentrated.

It should be noted that the high temporal resolution image in the present invention means that after processing, at least one phase of better quality time series data can be formed every 16 days. For example, a satellite provides one phase of data every 8 days, and there are two phases of data every 16 days. , if one of the phases is of good quality, the requirement is satisfied. Generally speaking, the higher the time resolution, the higher the probability of having a period of good quality time series data in 16 days. The high spatial resolution image means that the pixel size of the image is smaller than the plot scale. Generally speaking, the higher the spatial resolution, the better, generally less than 30m can meet the requirements. In terms of time resolution, high spatial resolution Resolution images require at least two high-quality images a year. NDVI stands for Normalized Difference Vegetation Index, which is a prior art.

In the land improvement project area, due to the large number of fields and the different cultivation conditions among the fields, the traditional field survey method is difficult to meet the dynamic monitoring requirements of crop planting area; Frequently, the growing seasons of different crops overlap. Based on the phenology and growth characteristics of a certain type of crops, the use of a few images can only realize the monitoring of the sown area of this type of crops, and it is difficult to realize the monitoring of the various plots of the year. Effective monitoring of the sown area of crops; moreover, the area of cultivated land in my country is relatively small, and the low spatial resolution images traditionally used at the regional scale cannot be directly applied to the plot scale due to the problem of mixed pixels. The method of the invention can avoid the above-mentioned shortcomings, and fill up the gap in the monitoring of the sowing area of crops at the plot scale in the land improvement project area.

The present invention is based on multi-source remote sensing data and uses a fusion algorithm to lay the data foundation for monitoring the sowing area of crops at the plot scale. Without this data basis, it is difficult to effectively obtain the sown area of crops at the plot scale in the historical period, because the remote sensing images generally used in the historical period have great limitations: when the temporal resolution of the image is high, its spatial resolution is low; and When the spatial resolution of the image is low, the temporal resolution is high. Without data fusion, it is difficult to provide data that conforms to plot-scale monitoring, and it is even more difficult to obtain information on the sown area of crops in historical plots.

On the basis of fused data, the peak value of each pixel can be obtained by using the second difference algorithm to extract the peak value, but there are often errors in peak value extraction, so it is necessary to pay special attention to the authenticity of the peak value when using the plot scale. The present invention proposes Based on the RGB images of Google Earth, the feature space of typical crops is extracted to identify the true and false peaks, which can improve the accuracy of peak recognition, and the feature space is easy to extract and use. Since the size of the fused pixel is generally smaller than the size of the plot, the present invention proposes to use block statistics and area ratio methods to obtain the average multiple cropping level of the plot on the basis of the multiple cropping level of the pixel. The product of the plot area is used to obtain the plot-scale crop sown area. In order to cooperate with land consolidation planning and fine management, the present invention proposes to use a spatial statistical method to detect the characteristic areas of dynamic changes in the crop sowing area based on the extraction of the sowing area of the crops at the plot scale. The invention can simply and accurately obtain the sown area of crops at the plot scale and its variation characteristics.

The present invention utilizes the respective advantages of high temporal resolution images and high spatial resolution images, and combines vector land use data to obtain the results of high temporal and spatial resolution NDVI of agricultural land plots in land improvement project areas; at the same time, it introduces RGB image spectral features, Texture features, extracting the characteristic space of paddy field, dry land, and irrigated crop planting; then using the quadratic difference algorithm and the plot statistical method to realize the extraction of the multi-cropping level of the land consolidation project area at the plot scale; finally realizing the land consolidation based on the spatial statistical method Extraction of the sown area of crops at the plot scale and its variation characteristics in the project area.

The present invention extracts the plot-scale crop sowing area and its variation characteristics in the land consolidation project area through a creative method, which can greatly save time, labor, and cost, and is beneficial to the fine management of agricultural land in the land consolidation project area and the improvement of land consolidation planning.

Description of drawings

The present invention will be further described below in conjunction with accompanying drawing.

Figure 1 is the RGB map of Google Earth in the land improvement project area.

Figure 2 is a vector land use type map in the land consolidation project area.

Figure 3 is a high-spatial-resolution NDVI time-series feature map of a typical crop planting area.

Figure 4 shows the multi-cropping level of crops at the plot scale in 2001 in the land consolidation project area.

Figure 5 shows the multi-cropping level of crops at the plot scale in 2010 in the land consolidation project area.

Figure 6 shows the sown area of crops at the plot scale in 2001 in the land consolidation project area.

Figure 7 shows the sown area of crops at the plot scale in 2010 in the land consolidation project area.

Figure 8 is a diagram of the change of crop sown area at the plot scale in the land consolidation project area.

Figure 9 is a characteristic area map of the change of crop sowing area at the plot scale in the land consolidation project area.

Detailed ways

Example

The method for monitoring the sowing area of crops at the plot scale in the land improvement project area of this embodiment includes the following steps:

Step 1. Obtain the high temporal resolution image, high spatial resolution image and vector land use data of the year to be monitored covering the land consolidation project area. The vector land use data includes the boundaries of cultivated land plots and land use types, as shown in Figure 2 Show.

In this embodiment, the land improvement project area is located in Nantong City, Jiangsu Province. The images used are free images from 2001 and 2010, among which the high temporal resolution images are Terra MODIS images, and the high spatial resolution images are LandsatOLI images. The Terra MODIS image data is one per day, with a spatial resolution of 250 meters; the cloud-free images selected for Landsat OLI data are two per year, with a spatial resolution of 30 meters; the scale of the vector land use data is 1:5000, and the accuracy can meet the requirements.

First, data preprocessing is performed on high temporal resolution images and high spatial resolution images, that is, radiometric correction, atmospheric correction, geometric correction, and projection transformation based on the projection space of vector land use data.

High temporal resolution NDVI and high spatial resolution NDVI are respectively obtained from high temporal resolution images and high spatial resolution images through band operations. In this embodiment, based on the near-infrared band reflectivity and red light The measurement analysis of the band reflectivity is obtained, and the calculation formula is:

NDVI=(NIR-RED)/(NIR+RED)

In the formula, NIR is the reflectance in the near-infrared band, and RED is the reflectance in the red band.

The high-time-resolution image Terra MODIS data is combined by maximum value and S-G filter algorithm to remove noise interference such as clouds and water vapor to form real NDVI data every 16 days.

Through the ESTARFM method, the high temporal resolution NDVI and the high spatial resolution NDVI are fused to obtain the high temporal and spatial resolution NDVI, and the time series data of the high temporal and spatial resolution image NDVI with a spatial resolution of 30 meters is obtained every 16 days.

Radiation correction, atmospheric correction, geometric correction, projection conversion, band calculation, maximum value synthesis, S-G filter algorithm, and ESTARFM (fusion) method are existing technologies, and you can refer to related books or literature on remote sensing image processing or software operation guide.

Step 2. Taking the boundary of the cultivated land as a constraint condition, the clipping tool in ArcGIS software is used to segment the high temporal and spatial resolution NDVI to obtain the high temporal and spatial resolution image NDVI of the agricultural land in the land consolidation project area, which is the follow-up farmland The typical regional characteristics of crops in dry land, irrigated land, and dry land provide a reasonable range, and make the boundary of the multi-cropping horizontal statistical unit extracted subsequently consistent with the cultivated land plots in the existing vector land use data.

According to the boundary of the cultivated land, combined with the high-resolution image (RGB image) in Google Earth, and the vegetation planting area identified according to the spectral and texture features, the typical area of crop planting in the land consolidation project area is obtained. The RGB image is shown in Figure 1. It can be downloaded from the Google Earth software platform.

According to the land use type, the typical crop planting area is divided into three types: paddy field, dry land and irrigated land, and the high temporal and spatial resolution NDVI time series features of these three types of typical crop planting areas are respectively extracted. The high temporal and spatial resolution NDVI Timing features include curve shape, curve peak and time distance between peaks formed by high spatio-temporal resolution NDVI time series data.

In this embodiment, the feature space is extracted based on the RGB image in Google Earth, and then the truth value discrimination rule is set: judge whether there is vegetation through the RGB image, and extract the corresponding land use in combination with the land use type (irrigated land, dry land, paddy field) Typical vegetation areas by type, combined with high spatial and temporal resolution NDVI to extract crop planting timing features (feature space). Through this feature space extraction method, the real value screening rules can be set instead of field surveys to obtain empirical values, which is practical and convenient. Among them, it is an existing technology to identify vegetation planting areas based on the spectrum and texture features of RGB images in Google Earth, and you can refer to "Google Earth-Based Forestry Map Production Method" (Author: Zhang Zhichao, Inner Mongolia Forestry Survey and Design Vol. 41 No. 3 , May 2018), or refer to "Remote Sensing Analysis of Land Use Types and Terrain Distribution of Pingtan Island Based on Google Earth Images" (Author: Gao Wei et al., Shelter Forest Science and Technology Issue 7 (178 issues), July 2018) .

Step 3. According to the high-temporal-spatial-resolution NDVI time-series characteristics of the typical areas where the three types of crops are planted, combine the quadratic difference algorithm to extract the peak value of each pixel in the high-spatial-temporal-resolution NDVI time-series characteristics, as shown in Figure 3. According to the peak size and the distance between the peaks to set the preset peak screening rules, and the number of peaks after screening is the multi-planting level of the pixel. The quadratic difference algorithm is an existing technology and can be realized by IDL software.

In this embodiment, when extracting the peak value of each pixel in the high-temporal-spatial resolution NDVI time-series feature, the peak value is judged as true or false: if a certain peak value is lower than 95% of the minimum NDVI peak value of paddy fields, agricultural land, and irrigated land, Or if the time interval between the peak and other peaks is less than 5 periods of data, the peak is considered to be a false peak and the peak is eliminated.

Step 4: Combining the boundaries of the cultivated land plots in the vector land use data, the multiple cropping level of the pixel is counted by plot, and the average multiple cropping level of each cultivated land plot is calculated by using the area ratio analysis method. The plot statistics and area ratio analysis were completed in the Zonal tool of ArcGIS and the Map Algebra tool respectively. The plot-scale multiple cropping levels obtained in 2001 and 2010 are shown in Figure 4 and Figure 5, respectively.

In order to verify the accuracy of the horizontal results of multiple cropping at the plot scale, field investigations were conducted on several of the plots, and the results showed that the accuracy met the requirements.

Step 5: Estimate the crop sown area of each cultivated land plot by the product of the area of each cultivated land plot and the average multiple cropping level, and calculate the crop sown area of the land consolidation project area by means of spatial statistical summation. The obtained plot-scale crop sown areas in 2001 and 2010 are shown in Figure 6 and Figure 7, respectively.

Comparing the sowing area in 2001 and 2010, the change of crop sowing area and the extraction of characteristic areas in the land consolidation project area were carried out, and the results are shown in Figure 8 and Figure 9, respectively. The specific method is: use the Map Algebra tool to calculate the change of crop sowing area at the plot scale, use the local spatial autocorrelation analysis method to obtain the changed characteristic area, separate and output the high-value aggregation area and the low-value aggregation area. High-value agglomeration areas represent concentrated areas with increased sown area; low-value agglomerated areas represent concentrated areas with reduced sown area.

The present invention is not limited to the specific technical solutions described in the above embodiments. Besides the above embodiments, the present invention can also have other implementation modes. For those skilled in the art, within the spirit and principles of the present invention, any technical solutions formed by any modifications, equivalent replacements, improvements, etc. shall be included in the protection scope of the present invention.

Claims (5)

1. A method for monitoring the plot scale crop seeding area of a land reclamation project area comprises the following steps:

acquiring a high-time resolution image, a high-time resolution image and vector land utilization data of a year to be monitored, which covers a land improvement project area, wherein the vector land utilization data comprises a cultivated land plot boundary and a land utilization type;

respectively obtaining high time resolution NDVI and high spatial resolution NDVI from the high time resolution image and the high spatial resolution image, and fusing the high time resolution NDVI and the high spatial resolution NDVI by an ESTARFM method to obtain high spatial resolution NDVI;

secondly, dividing the high spatial-temporal resolution NDVI by taking the farmland block boundary as a constraint condition to obtain a high spatial-temporal resolution image NDVI of the agricultural land in the land reclamation project area;

according to the boundary of a cultivated land plot, combining a Google Earth high-resolution image and identifying a vegetation planting area according to spectral and textural features to obtain a crop planting typical area of a land improvement project area;

dividing the typical crop planting area into three types of paddy fields, dry lands and irrigated water lands according to the land utilization type, and respectively extracting high space-time resolution NDVI time sequence characteristics of the three types of typical crop planting areas, wherein the high space-time resolution NDVI time sequence characteristics comprise a curve shape, a curve peak value and a time distance between wave peaks, which are formed by high space-time resolution NDVI time sequence data;

step three, extracting the peak value of each pixel in the high space-time resolution NDVI time sequence characteristics by combining a secondary difference algorithm according to the high space-time resolution NDVI time sequence characteristics of the typical planting areas of the three types of crops, setting a preset peak value screening rule according to the size of the peak value and the distance between the peak values, wherein the number of the screened peak values is the multiple planting level of the pixel;

step four, combining cultivated land block boundaries in the vector land utilization data to carry out block division statistics on the multiple planting level of the pixels, and calculating by adopting an area proportion analysis method to obtain the average multiple planting level of each cultivated land block;

and step five, estimating the crop seeding area of each cultivated land block according to the product of the area of each cultivated land block and the average multiple planting level, and counting the crop seeding area of the land improvement project area in a spatial statistics and summation mode.

2. The land improvement project area plot scale crop planting area monitoring method of claim 1, wherein: and step six, acquiring the space-time change of the crop sowing area of the land improvement project area on the land parcel scale by adopting an inter-annual difference value and a space autocorrelation analysis method, and separating and outputting a high-value gathering area and a low-value gathering area, wherein the high-value gathering area represents an area in which the land parcel with the increased crop sowing area is concentrated, and the low-value gathering area represents an area in which the land parcel with the decreased crop sowing area is concentrated.

3. The land improvement project area plot scale crop planting area monitoring method of claim 1, wherein: in the first step, firstly, the high-time resolution image and the high-space resolution image are preprocessed, wherein the preprocessing comprises radiation correction, atmospheric correction and geometric correction, and projection conversion is carried out by taking a vector land utilization data projection space as a reference.

4. The land improvement project area plot scale crop planting area monitoring method of claim 1, wherein: in the first step, after the high time resolution NDVI data are obtained, the high time resolution NDVI is reconstructed by adopting a maximum synthesis method and an S-G filtering algorithm, so that adjacent high time resolution NDVI arranged according to a time sequence have the same preset time interval.

5. The land improvement project area plot scale crop planting area monitoring method of claim 1, wherein: in the third step, when the peak value of each pixel in the high space-time resolution NDVI time sequence characteristic is extracted, the true and false judgment is carried out on the peak value, which is specifically as follows: and if a certain peak value is lower than 95% of the minimum value of the NDVI peak values of the paddy field, the agricultural land and the water-pouring land, or the time interval between the peak value and other peak values is smaller than a preset interval, the peak value is considered as a false peak value and the peak value is removed.

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