CN115311343A - Grassland pest occurrence area investigation method based on multi-stage sampling - Google Patents
Grassland pest occurrence area investigation method based on multi-stage sampling Download PDFInfo
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Abstract
The invention provides a grassland pest occurrence area investigation method based on multi-level sampling, which comprises the following steps: selecting a region suitable for the inhabitation of pests in the target region as a first-level investigation sample region; selecting areas with consistent community appearances and similar compositions from the first-level investigation sample area as second-level investigation sample areas, and dividing the second-level investigation sample areas into a plurality of grids as third-level investigation sample parties; extracting a plurality of three-level survey samples to carry out unmanned aerial vehicle aerial photography, and acquiring survey images; and calculating the pest occurrence area corresponding to the third-level investigation sample according to the investigation image, and calculating the pest occurrence area of the second-level investigation sample area and the first-level investigation sample area according to the pest occurrence area. The invention adopts a layered sampling mode, and avoids the defects that simple random sampling is excessively concentrated in a certain area, certain characteristics or certain characteristics are omitted; the hierarchical structure and the sampling proportion are reasonably set, so that the sampling result of the lower investigation region can better reflect the characteristics of the upper investigation region, and further the characteristics of the pest occurrence area of the whole target region are shown.
Description
Technical Field
The invention relates to the technical field of grassland pest control, in particular to a grassland pest occurrence area survey method based on multi-stage sampling.
Background
The grassland has important significance in the aspects of water and soil conservation, wind prevention and sand fixation, air purification, biodiversity maintenance and the like. In recent decades, natural grass has been degraded to varying degrees by co-interference with human activities and climate changes. The deteriorated grassland provides an excellent habitat for partial pests, such as rodents and the like, and promotes the population expansion of the pests. When the population density reaches a certain level, pest outbreaks result. Grassland pests such as grassland rats destroy vegetation and soil through a series of activities such as digging, feeding, bulldozing and the like, so that the damaged grassland weeds grow, the vegetation coverage is low, and the productivity is continuously reduced. Meanwhile, the frequent excavation and bulldozing actions of the soil and the grass lead the grassland to be spotty, reduce soil nutrients and aggravate water and soil loss, and cause negative effects on the ecological, landscape and economic values of the grassland. Pest problems have become one of the important obstacles to the protection, construction and utilization of natural turf.
For a long time, pest control is always in an emergency treatment situation of 'outbreak and extinction'. A large number of researches show that the balance of the grassland ecosystem is disturbed only when the population density is too high and the distribution range is too large, so that negative effects are generated. Thus, from an ecological point of view, the ultimate goal of pest control is not to perform "clean-of-population" killing, but to control their quantities within reasonable limits through scientific control. The important link of scientific prevention and control is to evaluate the damage of the grassland pests. The grassland pest evaluation factors comprise 'hazard grade (qualitative)' and 'hazard occurrence area (quantitative)', the research on the evaluation of the current hazard grade is more, but the exploration of the investigation method of the hazard occurrence area is still not a great breakthrough, and the hazard occurrence area is an important basis for establishing pest management strategies related to pest distribution range, monitoring and early warning, pest prevention and control expenditure planning and the like.
The pest occurrence area refers to an area where pest occurrence degree reaches a control index through various representative sample surveys. Taking the grassland mouse as an example, the grassland mouse damage occurrence area refers to the sum of a series of bare areas such as new and old soil dunes, soil dune flowing areas, holes, waste collapsed tunnels, obvious runways and bald spots caused by the grassland mouse activities through digging, bulldozing, feeding and other activities.
At present, the pest occurrence area survey mainly comprises a filling method and a drawing method. The filling method is that the measuring net is placed in the pest outbreak place, the damage condition is filled into the calculation paper according to the proportion by grids, the measuring net filling map is moved by grids, and finally the damage area is counted. The method has small sampling area, is labor-consuming and time-consuming, and is difficult to apply to large-area production practice investigation. The sketching method is that after the surveyor marks the pest occurrence place on the spot or visually observes the occurrence range, sketches the pattern spots on the working base map to estimate the occurrence area. The delineation method is limited by roads, terrains and the like, remote areas cannot be investigated, and the delineation precision is poor and the subjectiveness judgment is strong, so that the scientificity is poor. In addition, both the filling and sketching methods investigate the area where the pest has occurred, and random sampling cannot be achieved. These methods do not meet the statistical requirements when the degree of variation of the investigated population is large.
Grassland pests have strong habitat selectivity, which means that the habitat of pests has strong spatial heterogeneity and landscape complexity at the level of grassland landscape, and the traditional statistical method of simple sampling survey is not suitable for the survey. The development of the grassland pest occurrence area survey must comply with ecological and statistical principles.
Disclosure of Invention
In order to solve the technical problems, the invention provides a level sampling method, which is characterized in that firstly, a plurality of layers of the same landscape, such as mountains and flat ground of grasslands, are divided according to different landscapes, such as grasslands, bushes, wetlands and the like according to the characteristics of habitat of grassland pests. Then, a random sampling mode is used for sampling the sample units in each layer. And finally, counting the grassland pest occurrence area of the current layer according to the average grassland pest occurrence area of a plurality of sample units, and finally counting all layers to obtain the total pest occurrence area of the investigation region.
In order to achieve the technical purpose, the invention provides a grassland pest occurrence area investigation method based on multi-stage sampling, which comprises the following steps:
s1, selecting a region suitable for inhabitation of pests in a target region as a first-level investigation sample region;
s2, selecting areas with consistent community appearances and similar compositions in the primary investigation sample area as secondary investigation sample areas, and dividing the secondary investigation sample areas into a plurality of grids as tertiary investigation sample parties;
s3, extracting a plurality of the three-level survey samples to carry out unmanned aerial vehicle aerial photography, and acquiring survey images;
and S4, calculating the pest occurrence area corresponding to the third-level investigation sample according to the investigation image, and calculating the pest occurrence area of the second-level investigation sample area and the first-level investigation sample area according to the pest occurrence area.
In some preferred embodiments, the method for selecting an area suitable for habitation of pests as the first-order sample area in step S1 comprises:
the method comprises the steps of surveying and obtaining pest distribution point data of a target area, selecting an environment factor with large influence on the adaptability of a pest habitat, inputting the distribution point data and the environment factor into a species distribution model to obtain a plurality of pest adaptive regions with different adaptive factors, and selecting an area with the adaptive factor larger than 0.5 as a primary surveying sample region.
In some preferred embodiments, the method of selecting an environmental factor having a greater influence on the aptitude of a pest habitat is:
obtaining climate information, food information and geographic information closely related to the habitability of the pest habitat, establishing a correlation model, calculating and selecting an information index with the highest correlation as the environmental factor.
In some preferred embodiments, the extracting a plurality of the three-level survey samples in the step S3 to perform unmanned aerial vehicle aerial photography includes:
s31, randomly extracting a plurality of sample areas from the primary investigation sample area, calculating the mean value and variance of the vegetation coverage, and calculating the theoretical minimum sampling number n according to the following formula:
wherein d' is the allowable error of the sampling result, t is the standard error multiple, and w h The vegetation cover degree is the vegetation cover degree,
the variance of the vegetation coverage, N is the total number;
and S32, selecting an integer not less than the theoretical minimum sampling number n as the number of the sampling samples.
In some preferred embodiments, the method for calculating the pest occurrence area of the secondary survey area in step S4 comprises:
s41, calculating the average pest occurrence rate of the second-level survey sample:
wherein Di is the pest occurrence area of the ith tertiary survey sample; ai is the ith tertiary survey sample area; n' is the number of three-level survey samples；
And S42, calculating the total area of the secondary survey samples, and multiplying the average occurrence rate of the pests in the secondary survey samples to obtain the pest occurrence area of the secondary survey samples.
Advantageous effects
1. The invention adopts a layered sampling mode, and avoids the defects that simple random sampling is excessively concentrated in a certain area, certain characteristics or certain characteristics are omitted; 2. the hierarchical structure and the sampling proportion are reasonably set, so that the sampling result of the lower investigation region can better reflect the characteristics of the upper investigation region, and further the characteristics of the pest occurrence area of the whole target region are shown; 3. by combining the unmanned aerial vehicle aerial survey technology and the image recognition technology with layered sampling, the pest occurrence area can be accurately investigated on the spot, and a reliable data calculation basis is obtained.
Drawings
FIG. 1 is a schematic flow diagram of a preferred embodiment of the present invention;
FIG. 2 is a diagram illustrating the result of partitioning the first-level survey sample areas according to a preferred embodiment of the present invention;
FIG. 3 is a diagram of the results of the division of the secondary survey sample and the tertiary survey sample in a preferred embodiment of the present invention;
FIG. 4 is a diagram of the interpretation result of the survey image in a preferred embodiment of the present invention;
FIG. 5 is a diagram illustrating an error relationship between measured values and predicted values of the interpretation results of the survey images according to a preferred embodiment of the present invention;
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described below with reference to the accompanying drawings. In the description of the present invention, it is to be understood that the terms "upper", "lower", "front", "rear", "left", "right", "top", "bottom", "inner", "outer", and the like, indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, are merely for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention.
As shown in figure 1, the invention provides a grassland pest occurrence area survey method based on multi-stage sampling, which comprises the following steps:
s1, selecting a region suitable for inhabitation of pests in a target region as a first-level investigation sample region; the first-level investigation sample area is a sampling total area for investigating pest occurrence area. The pests refer to main pests (including invasive species) which harm grassland vegetation and products thereof and cause economic or ecological loss, and include rodents, insects, plant pathogenic microorganisms, toxic and harmful grasses and the like, wherein grassland mouse and rabbit pests are the most common pests. The pest occurrence area refers to an area where the pest occurrence degree reaches a control index through various representative sample samples. Taking the grassland mouse as an example, the grassland mouse damage occurrence area refers to the sum of a series of bare areas such as new and old soil dunes, soil dune flowing areas, holes, waste collapsed tunnels, obvious runways and bald spots caused by the grassland mouse activities through digging, bulldozing, feeding and other activities.
There are many methods for selecting the area where the harmful organisms inhabit, and the methods usually include historical data retrieval, confirmation after field exploration and the like, but most of the methods for selecting the area where the harmful organisms inhabit depend on empirical data, namely the methods can only be used for the area where the harmful organisms have occurred or are occurring, and the methods cannot be well adapted to the area where the harmful organisms do not occur but have potential risks. Thus, in some preferred embodiments, there is provided a more scientific method of selecting a suitable habitat for pests, comprising:
the method comprises the steps of surveying and obtaining pest distribution point data of a target area, selecting an environment factor with large influence on the adaptability of a pest habitat, inputting the distribution point data and the environment factor into a species distribution model to obtain a plurality of pest adaptive regions with different adaptive factors, and selecting an area with the adaptive factor larger than 0.5 as a primary surveying sample region.
Wherein, the survey points are selected to set a stepping route under the condition of considering traffic conditions, and the stepping route needs to penetrate through the grassland class, the terrain and the like in the survey area as far as possible. And (5) marking coordinate points on the places where the harmful organisms are distributed in the investigation process and recording the longitude and latitude of the places. The environmental factors having a large influence on the adaptability of the habitat of the pests specifically include indexes such as climate information (e.g., temperature, precipitation), food information (e.g., biomass, coverage), habitat geographic information (e.g., terrain and elevation) and the like closely related to the pests to be investigated, and the index information may be obtained by using a field investigation and sampling method or by searching a network database (e.g., a meteorological network and a Chinese academy database).
The Species Distribution Models (SDMs) are mathematical Models based on Species existence or abundance data and environmental factor data, the Models estimate the ecological niche requirements of Species according to statistical information provided by sampling points in a multidimensional ecological space composed of environmental factors, and project the ecological niche requirements to a selected space-time range to reflect the preference degree of the Species to the habitat in a probability form, and generally provide simulation results by using boolean values (1 represents potential Distribution and 0 represents potential non-Distribution), and the model results generally reflect the Distribution of the Species suitable for the habitat on a large scale.
S2, selecting areas with consistent community appearances and similar compositions in the first-level survey sample area as second-level survey sample areas, and dividing the second-level survey sample areas into a plurality of grids serving as third-level survey sample parties; the community appearance is consistent, namely that areas where pests of the same kind live are generally the same in characteristics, such as wide flat ground and gentle slope (gradient <7 ℃) suitable for the plateau rats and rabbits to inhabit; the compositional similarity refers to the type of grassland, and generally speaking, grassland plants are of a single type and do not have drastic changes or significant differences. It should be understood that, when the grid is divided in the secondary survey mode, the size of the grid needs to be determined by considering the actual area size of the target area, the model of the unmanned aerial vehicle to be used in the subsequent steps, and the image interpretation software and hardware computing resource condition, specifically, when the actual area of the target area is large, the model of the unmanned aerial vehicle is high, and the image interpretation software and hardware computing power is sufficient, a larger grid can be divided, and a smaller grid can be divided, otherwise, the specific division method is determined by those skilled in the art according to the actual situation, and the present invention is not limited further.
S3, extracting a plurality of the three-stage survey samples to carry out unmanned aerial vehicle aerial photography, and acquiring survey images; those skilled in the art should know that the accuracy of the final survey result relative to the overall real value is directly related to the sample capacity, and theoretically, all three-level survey samples should be investigated in the field, but in reality, the human and financial resources are not so much to support such extensive field sampling, so that those skilled in the art should adopt a sampling mode to carry out field survey on the three-level survey samples, wherein the specific number of the extracted samples can be determined by those skilled in the art according to the needs and expenses of the project. The existing unmanned aerial vehicle low-altitude remote sensing technology can ensure that a sufficiently clear aerial image can be obtained, and by combining the existing image interpretation technology (an image processing technology for dividing a characterized pest occurrence area from a normal grassland), ground characteristics such as caves, soil dunes and bare spots dug by pests can be rapidly identified, and the quantity and the area can be calculated, so that the field statistical mapping can be carried out without spending a large amount of manpower and material resources, the unmanned aerial vehicle low-altitude remote sensing technology is suitable for obtaining the grassland pest occurrence area in a large range, accords with the ecological and statistical principles, and has the advantages of high efficiency, strong operability and scientificity, and the like.
In some preferred embodiments, in order to save more manpower and financial resources, a lower sampling number needs to be set, but the sampling number needs to be sufficient enough to meet higher sampling precision, so that the actual value of the whole body is measured more accurately, and the embodiment provides a sampling number determination method which can be considered by all parties, and the method comprises the following steps:
s31, randomly extracting a plurality of sample areas from the primary investigation sample area, calculating the mean value and variance of the vegetation coverage, and calculating the theoretical minimum sampling number n according to the following formula:
wherein, d'For error allowance of sampling result, t is standard error multiple, w h The degree of coverage of the vegetation is,
the variance of the vegetation coverage, N is the total number;
and S32, selecting an integer not less than the theoretical minimum sampling number n as the number of the sampling samples.
And S4, calculating the pest occurrence area corresponding to the third-level investigation sample according to the investigation image, and calculating the pest occurrence area of the second-level investigation sample area and the first-level investigation sample area according to the pest occurrence area.
The calculation of the pest occurrence area based on the survey image may be performed by image interpretation calculation using existing pest monitoring software, such as lawn rodent monitoring software v1.0 with registration number 2021SR 0546708. It should be understood that the emphasis of image interpretation will also be different for different kinds of pests, for example, when surveying the area where a zokor hazard occurs, the emphasis of image interpretation is put on the identification of new and old dunes and dune runoff area caused by zokor activity; when investigating the area where big gerbils are damaged, because of the particularity of desert grassland, the image interpretation is mainly put on the cave entrance and the cave entrance roundabout caused by the movement of the gerbils.
In some preferred embodiments, a method for calculating pest occurrence area of a secondary survey area is provided, comprising:
s41, calculating the average pest occurrence rate of the secondary investigation sample plot:
wherein Di is the pest occurrence area of the ith third-level survey sample; ai is the ith tertiary survey sample area; n' is the number of the third-level survey samples;
and S42, calculating the total area of the secondary survey sample plots, and multiplying the average pest occurrence rate of the secondary survey sample plots to obtain the pest occurrence area of the secondary survey sample plots.
Examples
In this example, the area of rabbit injury in rat was investigated on grassland of Maqu county, gansu province.
1. First order survey sample area determination
The main environmental influence factors of the investigation region are obtained through the database, and the temperature, the precipitation and the food resource (replaced by the normalized difference vegetation index) are selected to participate in modeling. The species distribution model is a Maxent model. The calculation result shows that the high survival region (survival index) of the plateau mouse rabbit in the current environmental climate is shown in figure 2>0.5 Mainly focused on X1, X2, X3, X4, etc. According to the scheme design, the survival index>0.5 area is extracted as the first-class investigation sample area of the area where the mouse damage occurs, and the area is 2.5 multiplied by 10 5 hm 2 。
2. Second level survey plot delineation
After the primary survey sample area is determined, secondary survey sample areas are divided. The field investigation shows that the plateau mouse and rabbit habitat is located on the open flat ground and gentle slope (gradient)<7 degrees) and grass type singleness. Therefore, in this embodiment, a flat ground or a gentle slope is used as a main investigation region. A 5km × 5km grid is created as three-level survey samples based on the ArcGIS 10.2 fisheret analysis tool, and 175 three-level survey samples are divided in a first-level survey sample area in 4 towns as shown in fig. 3. Before formal investigation, 10 three-level sample plots are randomly selected for preliminary experiments to obtain the mean value and the variance of the vegetation coverage, the allowable error of the sampling result of the embodiment is set to be 8%, the standard error multiple is set to be 95%, and finally the method is passed
When the sampling precision is 92%, the theoretical minimum sampling number n =31.43, namely, at least 32 samples need to be selected for investigation. Therefore, after the grid is constructed, the embodiment selects 38 blocks of the same sample rate, namely 21.7%, for unmanned aerial vehicle low-altitude aerial photography.
3. Calculation of area of mouse damage
The acquired investigation image is interpreted by using independently designed grassland mouse damage monitoring software v1.0 (registration number: 2021SR 0546708), the ground features such as rat caves, soil dunes, bare spots and the like are rapidly identified, the number and the area are calculated, as shown in figure 4, after the vegetation coverage of each investigation sample is obtained, the unmanned aerial vehicle image interpretation precision is verified, and through verification, as shown in figure 5, the average relative error MRE =0.07 between an actual measurement value and a predicted value and the correlation coefficient R =0.81 are found, so that the unmanned aerial vehicle image interpretation value and an artificial actual measurement value in the research area are highly matched.
In order to judge whether the target area reaches the level of taking prevention and treatment measures, based on the index of preventing and treating the rats and rabbits in the standard of preventing and treating the grassland pests, the area of the bald spots and the number of effective holes in the aerial images are counted respectively, wherein any index is in accordance with the index and is regarded as reaching the prevention and treatment index. After the plateau mice and rabbits are interpreted to obtain the occurrence areas of different third-level survey sample prescriptions, the formula is followed
Calculating the incidence rate of the plateau mouse rabbit in the second-level investigation sample plot, further calculating the incidence area of the plateau mouse rabbit in the first-level investigation sample plot, and adding the incidence areas of the plateau mouse rabbit in the different first-level investigation sample plots to obtain the total incidence area of the plateau mouse rabbit in the investigation region as shown in the following table 1: 1.8X 10 4 hm 2 Accounting for 7.2 percent of the area of the total survival area.
TABLE 1 area of investigation region where mouse damage occurs
The foregoing shows and describes the general principles, principal features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (5)
1. A grassland pest occurrence area survey method based on multi-stage sampling is characterized by comprising the following steps:
s1, selecting a region suitable for inhabitation of pests in a target region as a first-level investigation sample region;
s2, selecting areas with consistent community appearances and similar compositions in the primary investigation sample area as secondary investigation sample areas, and dividing the secondary investigation sample areas into a plurality of grids as tertiary investigation sample parties;
s3, extracting a plurality of the three-stage survey samples to carry out unmanned aerial vehicle aerial photography, and acquiring survey images;
and S4, calculating the pest occurrence area corresponding to the third-level investigation sample according to the investigation image, and calculating the pest occurrence area of the second-level investigation sample area and the first-level investigation sample area according to the pest occurrence area.
2. The method for investigating the area where grassland harmful organisms occur based on multi-stage sampling according to claim 1, wherein the method for selecting an area suitable for inhabitation of harmful organisms as the first-stage sample area in step S1 comprises:
the method comprises the steps of surveying and obtaining pest distribution point data of a target area, selecting an environment factor with large influence on the adaptability of a pest habitat, inputting the distribution point data and the environment factor into a species distribution model to obtain a plurality of pest adaptive regions with different adaptive factors, and selecting an area with the adaptive factor larger than 0.5 as a primary surveying sample region.
3. A method of investigating the area of occurrence of grassland pests based on multi-stage sampling according to claim 2, wherein the method of selecting the environmental factor having a greater influence on the adaptability of the habitat of the pests is:
obtaining climate information, food information and geographic information closely related to the habitability of the pest habitat, establishing a correlation model, and calculating and selecting an information index with the highest correlation as the environmental factor.
4. A method for investigating the pest occurrence area on a grassland based on multi-level sampling according to claim 1, wherein the step S3 comprises extracting a plurality of three-level survey samples for unmanned aerial vehicle aerial photography, and the method for determining the specific number of extracted samples comprises:
s31, randomly extracting a plurality of sample areas from the first-stage survey sample area, calculating the mean value and the variance of the vegetation coverage, and calculating the theoretical minimum sampling number n according to the following formula:
wherein d' is the allowable error of the sampling result, t is the multiple of the standard error, and w h The vegetation cover degree is the vegetation cover degree,
the variance of the vegetation coverage, N is the total number;
and S32, selecting an integer not less than the theoretical minimum sampling number n as the number of the sampling samples.
5. The method for investigating the pest occurrence area based on multi-stage sampling according to claim 1, wherein the method for calculating the pest occurrence area of the second-stage survey sample in step S4 comprises:
s41, calculating the average pest occurrence rate of the secondary investigation sample plot:
wherein Di is the pest occurrence area of the ith third-level survey sample; ai is the ith tertiary survey sample area; n' is the number of the third-level survey samples;
and S42, calculating the total area of the secondary survey samples, and multiplying the average occurrence rate of the pests in the secondary survey samples to obtain the pest occurrence area of the secondary survey samples.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116403125A (en) * | 2022-12-20 | 2023-07-07 | 祁连山国家公园青海服务保障中心 | Grassland caterpillar suitable living area dividing method, system and terminal based on unmanned aerial vehicle aerial photography |
| CN116778334A (en) * | 2023-06-28 | 2023-09-19 | 中国农业大学 | Quantitative large-scale space grassland mouse entrance density prediction method and system |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116403125A (en) * | 2022-12-20 | 2023-07-07 | 祁连山国家公园青海服务保障中心 | Grassland caterpillar suitable living area dividing method, system and terminal based on unmanned aerial vehicle aerial photography |
| CN116403125B (en) * | 2022-12-20 | 2024-05-14 | 祁连山国家公园青海服务保障中心 | Grassland caterpillar suitable living area dividing method, system and terminal based on unmanned aerial vehicle aerial photography |
| CN116778334A (en) * | 2023-06-28 | 2023-09-19 | 中国农业大学 | Quantitative large-scale space grassland mouse entrance density prediction method and system |
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