A method for soil and water loss risk assessment in newly developed orchards based on drone aerial photography

Abstract

The present invention discloses a method for assessing the risk of soil and water loss in a newly developed orchard based on drone aerial photography, including: obtaining DOM and DSM based on visible light drone aerial photography images of newly developed orchards with concentrated and continuous distribution; calculating green leaf index and vegetation coverage index; calculating coverage and biological measure factors of CSLE model; using CSLE model to calculate soil erosion modulus and perform soil erosion intensity classification; using soil erosion intensity grade to characterize the in-situ impact grade of soil and water loss; calculating terrain roughness index weight and vegetation coverage and management factor weight; calculating sediment connectivity index and dividing sediment connectivity grade; using sediment connectivity grade to characterize the ex-situ impact grade of soil and water loss; weighing the in-situ impact and ex-situ impact of soil and water loss, and assessing the risk grade of soil and water loss. The present invention can improve the accuracy of soil and water loss risk assessment results, and meet the urgent need for soil and water loss risk assessment of newly developed orchards with concentrated and continuous distribution.

Description

Technical Field

[0001] The invention relates to the field of soil and water conservation, and in particular to a soil and water loss risk assessment method for a newly developed orchard based on unmanned aerial vehicle (UAV) aerial photography. Background Art

[0002] Soil and water loss risk usually refers to the adverse effects that soil erosion and land degradation caused by natural factors and human activities may have on the ecological environment, agricultural production and socio-economic development within a certain time and space range. It reflects the possibility of soil and water loss under specific conditions and its potential negative impact. Soil and water loss risk assessment helps to understand the spatial differences in soil and water loss in the assessment area and provides a scientific basis for the rational allocation of soil and water conservation measures and the formulation of soil and water conservation policies. By identifying high-risk areas, soil and water loss prevention and control work can be carried out in a targeted manner to improve governance efficiency.

[0003] Soil and water loss risk assessment is mainly divided into two categories: qualitative assessment and quantitative assessment.

[0004] Qualitative assessment mainly determines the possibility of soil erosion through multi-factor comprehensive evaluation and expert experience.

[0005] Quantitative assessment mainly relies on mathematical models and statistical data, and can provide more accurate risk predictions. Commonly used models include CSLE (Chinese Soil Loss Equation), USLE (Universal Soil Loss Equation) and RUSLE (Revised Universal Soil Loss Equation).

[0006] In the process of implementing the technical solution of the embodiment of the present invention, the inventor of this patent found that the prior art has at least the following technical problems:

[0007] The existing qualitative assessment of soil and water loss risk is suitable for situations with low risk or insufficient data, and is highly subjective and requires high standards of assessment experts.

[0008] The existing quantitative assessment methods for soil and water loss risk only consider the possibility of soil and water loss, but lack consideration of its potential negative impacts, especially the ex situ negative impacts caused by soil and water loss. In addition, the existing soil and water loss risk assessment is mainly carried out on large-scale areas such as regions and river basins or production and construction projects. Newly developed orchards are an important source area of ​​man-made soil and water loss, and are also the focus and difficulty of regional soil and water conservation supervision and soil and water loss control. However, there has been no public report on the soil and water loss risk assessment of newly developed orchards.

[0009] In summary, the existing soil and water loss risk assessment methods cannot meet the actual use needs of soil and water loss risk assessment in the process of refined soil and water conservation supervision and soil and water loss control. Summary of the invention

[0010] The embodiment of the present invention provides a soil and water loss risk assessment method for a newly developed orchard based on drone aerial photography, which solves the problem that the existing soil and water loss risk assessment method cannot meet the actual use needs of soil and water loss risk assessment in the process of refined soil and water conservation supervision and soil and water loss control.

[0011] An embodiment of the present invention provides a method for assessing soil and water loss risk in a newly developed orchard based on drone aerial photography. The method is applicable to newly developed orchards that are concentrated and contiguously distributed, and includes:

[0012] Obtain DOM and DSM based on visible light drone aerial images of newly developed orchards with concentrated and contiguous distribution;

[0013] Based on DOM, the green leaf index is used instead of the normalized vegetation index to estimate the vegetation coverage index. The calculation formula is as follows:

[0014] ,

[0015] ,

[0016] In the formula, GLI is the green leaf index, Red , Green , Blue They are the DN values ​​of the red, green and blue bands in DOM. FVC is the vegetation coverage index, To evaluate the maximum value of the green leaf index of vegetation in the area, The minimum green leaf index of bare soil in the assessment area;

[0017] Based on the vegetation coverage index, the coverage and biological measure factors of the CSLE model were calculated using the segmented estimation equation for coverage and biological measure factors suitable for orchard soil and water loss estimation. The calculation formula is as follows:

[0018] ,

[0019] In the formula, B for coverage and biological measures factors;

[0020] The CSLE model is used to estimate the soil erosion modulus and to classify the soil erosion intensity. The soil erosion intensity grade is used to characterize the in-situ impact grade of soil and water loss, which includes no impact, low impact, medium impact and high impact.

[0021] Based on DSM, the weight of terrain roughness index is calculated; based on vegetation coverage index, the weight of vegetation coverage and management factor is calculated; the calculation formulas of terrain roughness index weight and vegetation coverage and management factor weight are as follows:

[0022] ,

[0023] ,

[0024] In the formula, is the terrain roughness index weight, RI is the terrain roughness index, To evaluate the maximum value of the terrain roughness index in the area, is the weight of vegetation cover and management factor, C is the vegetation cover and management factor;

[0025] Taking into account the influence of topography and vegetation, the sediment connectivity index is calculated, and the calculation formula is as follows:

[0026] ,

[0027] In the formula, IC is the sediment connectivity index, D up and D dn are the upslope and downslope parts of sediment connectivity, and are the average terrain roughness index weight and average vegetation cover and management factor weight of the upstream contribution area, is the average slope of the upstream contributing area, A is the area of ​​the upstream contribution region, d i DSM i The length of the runoff path along the steepest downslope direction for each grid, , and S i Respectively i The weight of the terrain roughness index, the weight of the vegetation cover and management factor, and the slope of each grid;

[0028] Based on the sediment connectivity index, the sediment connectivity level is used to characterize the level of soil and water loss ex situ impact, which includes low impact, medium impact and high impact;

[0029] Based on the in-situ impact level of soil and water loss and the ex-situ impact level of soil and water loss, the in-situ-ex-situ impact trade-off relationship of soil and water loss is generated through spatial overlay analysis, and the soil and water loss risk level is evaluated.

[0030] Optionally, based on the in-situ impact level of soil and water loss and the ex-situ impact level of soil and water loss, a trade-off relationship between in-situ and ex-situ impacts of soil and water loss is generated through spatial overlay analysis to assess the risk level of soil and water loss, specifically including:

[0031] When the in-situ impact level of soil and water loss is high, and the ex-situ impact level of soil and water loss is high or medium, the soil and water loss risk level is high risk;

[0032] When the in-situ impact level of soil and water loss is high impact and the ex-situ impact level of soil and water loss is low impact, or when the in-situ impact level of soil and water loss is medium impact and the ex-situ impact level of soil and water loss is high impact, the soil and water loss risk level is medium-high risk;

[0033] When the in-situ impact level of soil and water loss is medium, and the ex-situ impact level of soil and water loss is medium, the soil and water loss risk level is medium risk;

[0034] When the in-situ impact level of soil and water loss is medium impact and the ex-situ impact level of soil and water loss is low impact, or when the in-situ impact level of soil and water loss is low impact and the ex-situ impact level of soil and water loss is high impact, the soil and water loss risk level is medium-low risk;

[0035] When the in-situ impact level of soil and water loss is low impact, and the ex-situ impact level of soil and water loss is medium impact or low impact, the soil and water loss risk level is low risk;

[0036] When the in-situ impact level of soil and water loss is no impact, and the ex-situ impact level of soil and water loss is high impact, medium impact or low impact, the soil and water loss risk level is no risk.

[0037] Optionally, the soil erosion intensity grade is used to characterize the in-situ impact grade of soil and water loss, specifically:

[0038] When the soil erosion intensity level is slight erosion, the in-situ impact level of soil and water loss is no impact;

[0039] When the soil erosion intensity level is mild erosion, the in-situ impact level of soil and water loss is low impact;

[0040] When the soil erosion intensity level is moderate erosion, the in-situ impact level of soil and water loss is medium impact;

[0041] When the soil erosion intensity level is strong erosion, extremely strong erosion or severe erosion, the in-situ impact level of soil and water loss is high impact.

[0042] Optionally, the sediment connectivity level is used to characterize the level of ex situ impact of soil erosion, specifically:

[0043] When the sediment connectivity level is low connectivity, the ex situ impact level of soil erosion is low impact;

[0044] When the sediment connectivity level is medium connectivity, the level of soil and water loss ectopic impact is medium impact;

[0045] When the sediment connectivity level is high connectivity, the ex situ impact level of soil erosion is high impact.

[0046] Optionally, classification based on sediment connectivity index, including:

[0047] The difference between the mean of the sediment connectivity index and the standard deviation of the sediment connectivity index was determined as the lower boundary of the sediment connectivity index;

[0048] The sum of the mean value of the sediment connectivity index and the standard deviation of the sediment connectivity index is determined as the upper boundary of the sediment connectivity index;

[0049] When the sediment connectivity index is less than the lower boundary of the sediment connectivity index, the sediment connectivity level is low connectivity; when the sediment connectivity index is between the lower boundary of the sediment connectivity index and the upper boundary of the sediment connectivity index, the sediment connectivity level is medium connectivity; when the sediment connectivity index is greater than the lower boundary of the sediment connectivity index, the sediment connectivity level is high connectivity.

[0050] Optionally, the terrain roughness index is calculated as follows:

[0051] ,

[0052] In the formula, RI is the terrain roughness index, n 2 is the number of pixels in the n×n sliding window, x i is the elevation value of a specific pixel in the sliding window, x m n 2 The average elevation of pixels.

[0053] Optionally, the calculation formula of the soil erosion modulus is as follows:

[0054] ,

[0055] Where A is the soil erosion modulus, R is the rainfall erosivity factor, K is the soil erodibility factor, L is the slope length factor, S is the slope factor, B is the cover and biological measures factor, E is the engineering measures factor, and T is the tillage measures factor.

[0056] One or more technical solutions provided in the embodiments of the present invention have at least the following technical effects or advantages:

[0057] First, in view of the practical needs of soil and water loss risk assessment in the process of refined soil and water conservation supervision and soil and water loss control in newly developed orchards, in view of the lack of quantitative assessment of soil and water loss risks in newly developed orchards during the standardized remote sensing supervision of mountain forest and fruit development, the existing commercial remote sensing images are difficult to meet the terrain and vegetation coverage data requirements for soil and water loss risk assessment in newly developed orchards. The present invention can meet the urgent needs of soil and water loss risk assessment for newly developed orchards with concentrated and contiguous distribution, and the reasons are as follows:

[0058] 1. The CSLE model and sediment connectivity index used in the present invention are both calculation methods based on raster data. In principle, the methods have no restrictions on applicable objects. The soil and water loss risk assessment method of the present invention is based on the spatial overlay analysis of the soil erosion intensity level (corresponding to the in-situ impact level of soil and water loss) obtained by calculating the CSLE model and the sediment connectivity level (corresponding to the ex-situ impact level of soil and water loss) calculated by the sediment connectivity index. In principle, the present invention has no restrictions on applicable objects.

[0059] 2. The existing technology has applied the CSLE model to the simulation of soil erosion in orchards, proving that the CSLE model and its parameter estimation method have been recognized by the academic community in the field of soil and water conservation in terms of soil erosion estimation in orchards, and the vegetation coverage and management factor weight calculation method involved in the calculation of the sediment connectivity index is also applicable to orchards;

[0060] 3. Since the CSLE model factor parameter estimation method for soil erosion simulation and estimation in orchards has been recognized by the academic community, the biggest limitation of soil erosion simulation and sediment connectivity index calculation in orchards is that the open source terrain data (DEM, spatial resolution is mostly 30m, up to 12.5m) and DNVI data (Landset series NDVI data, spatial resolution 30m; MODIS series NDVI data, spatial resolution 250m) usually used for soil erosion simulation and sediment connectivity calculation are too low, which is difficult to meet the data accuracy requirements for soil erosion simulation and sediment connectivity index calculation in newly developed orchards with a scale of less than 1 square kilometer (maximum no more than 10 square kilometers). The present invention uses commonly used visible light drones to obtain DOM and DSM data of newly developed orchards by aerial photography, which can provide sub-meter or even centimeter-level high-resolution terrain and image data for soil erosion simulation and sediment connectivity index calculation in newly developed orchards. Taking into account that commonly used drones are equipped with visible light lenses, their visible light aerial images lack near-infrared bands and cannot estimate the normalized vegetation index, the green leaf index extracted from visible light aerial images is used instead of the normalized vegetation index to calculate the vegetation coverage index, which makes up for the lack of infrared bands in visible light drone aerial images and solves the problem of estimating CSLE model coverage and biological measure factors and USLE / RUSLE model vegetation coverage and management factors based on visible light drone aerial images. It provides fine data (spatial resolution less than 1m) for the calculation of soil erosion modulus and sediment connectivity index in newly developed orchards, which can greatly improve the accuracy of soil erosion simulation and sediment connectivity index calculation in newly developed orchards, and can effectively support the refined soil and water conservation supervision and soil and water loss control business practices in concentrated and contiguous newly developed orchards.

[0061] Secondly, newly developed orchards are important source areas of man-made soil erosion, and are also the focus and difficulty of regional soil and water conservation supervision and soil erosion control. Their soil erosion may lead to a series of serious ex situ impacts such as damage to farmland at the foot of the slope or the bottom of the valley, siltation in rivers and reservoirs, and water pollution. At the same time, soil and water conservation measures such as vegetation and terraces prevent soil erosion essentially through the synergistic effect of in situ erosion reduction effect and ex situ erosion reduction effect. In view of this, whether from the perspective of systematic and comprehensive assessment of the impact of soil erosion, or from the perspective of scientific guidance of the layout of soil and water conservation measures based on the assessment results, it is urgently necessary to comprehensively consider the in situ and ex situ impacts of soil erosion to carry out soil erosion risk assessment for newly developed orchards. Based on the existing quantitative assessment method for soil erosion risk, the present invention introduces the sediment connectivity index into the field of soil erosion risk assessment for the first time, which makes up for the deficiency that the existing quantitative assessment method for soil erosion risk does not consider the ex situ impact of soil erosion, and can improve the accuracy and application value of the soil erosion risk assessment results. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1It is a schematic diagram of DOM and DSM of a newly developed orchard with concentrated and continuous distribution in one embodiment of the present invention;

[0063] Figure 2 A land use status distribution map of a newly developed orchard distributed in a concentrated and contiguous area in one embodiment of the present invention;

[0064] Figure 3 A terrain roughness index distribution map of a newly developed orchard with concentrated and continuous distribution in one embodiment of the present invention;

[0065] Figure 4 A distribution map of green leaf index and vegetation coverage of a newly developed orchard with concentrated and continuous distribution in one embodiment of the present invention;

[0066] Figure 5 A distribution diagram of slope factor and slope length factor of a CSLE model of a newly developed orchard with concentrated and continuous distribution in one embodiment of the present invention;

[0067] Figure 6 The CSLE model B factor, E factor and T factor distribution diagram of a newly developed orchard with concentrated and continuous distribution in one embodiment of the present invention;

[0068] Figure 7 A soil erosion modulus distribution map of a newly developed orchard with concentrated and continuous distribution in one embodiment of the present invention;

[0069] Figure 8 A distribution map of terrain roughness index weight, vegetation coverage and management factor weight of a newly developed orchard with concentrated and continuous distribution in one embodiment of the present invention;

[0070] Fig. 9 A distribution map of the sediment connectivity index of a newly developed orchard with concentrated and continuous distribution in one embodiment of the present invention;

[0071] Fig.10 A distribution map of the in-situ impact of soil erosion and the ex-situ impact of soil erosion in a newly developed orchard distributed in a concentrated and contiguous area in one embodiment of the present invention;

[0072] Fig.11 A distribution diagram of the in-situ-ex-situ impact trade-off relationship and soil and water loss risk level of a newly developed orchard with concentrated and continuous distribution in one embodiment of the present invention;

[0073] Fig.12 The present invention is a simplified flowchart of a newly developed orchard soil and water loss risk assessment method based on drone aerial photography in one embodiment of the present invention. DETAILED DESCRIPTION

[0074] The embodiment of the present invention provides a soil and water loss risk assessment method for a newly developed orchard based on drone aerial photography, which solves the problem that the existing soil and water loss risk assessment method cannot meet the actual use needs of soil and water loss risk assessment in the process of refined soil and water conservation supervision and soil and water loss control.

[0075] In order to better understand the newly developed orchard soil erosion risk assessment method based on drone aerial photography, the following will be described in detail in conjunction with the drawings and specific implementation methods of the specification. Obviously, the embodiments described in the present invention are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0076] like Fig.12 As shown, a method for assessing the risk of soil erosion in a newly developed orchard based on drone aerial photography in an embodiment of the present invention mainly includes the following steps: obtaining DOM and DSM based on visible light drone aerial images of newly developed orchards with concentrated and continuous distribution; calculating the green leaf index and vegetation coverage index; calculating the coverage and biological measure factors of the CSLE model; using the CSLE model to calculate the soil erosion modulus and classify the soil erosion intensity; using the soil erosion intensity grade to characterize the in-situ impact level of soil erosion; calculating the terrain roughness index weight and the vegetation coverage and management factor weight; calculating the sediment connectivity index and dividing the sediment connectivity grade; using the sediment connectivity grade to characterize the ex-situ impact level of soil erosion; weighing the in-situ impact and ex-situ impact of soil erosion, and evaluating the soil erosion risk level.

[0077] The embodiment of the present invention provides a method for assessing the risk of soil and water loss in a newly developed orchard based on drone aerial photography. The object of application is a newly developed orchard with concentrated and continuous distribution, that is, a newly developed orchard with a certain scale and distributed on multiple adjacent hillsides. The concentrated and continuous distribution mainly highlights the concentrated distribution and large area of ​​the newly developed orchards. The risk of soil and water loss in the newly developed orchards with large areas and concentrated distribution is high, and the need for conducting soil and water loss risk assessment is more urgent. In view of this, the following will take a newly developed orchard with concentrated and continuous distribution in Jiangxi Province in the south as an example to explain in detail steps S1 to S10.

[0078] Step S1: Obtain DOM and DSM based on visible light drone aerial images of newly developed orchards with concentrated and contiguous distribution.

[0079] In the specific implementation process, for example, a newly developed orchard with concentrated and contiguous distribution is used as the evaluation area, and a DJI Phantom 4 RTK drone equipped with a visible light camera is used to carry out aerial photography by planning routes to obtain high-definition orthophotos of the evaluation area, that is, to obtain visible light drone aerial images. With the help of DJI Terra software, a DOM (Digital Orthophoto Map) and DSM (Digital Surface Model) of a newly developed orchard with concentrated and contiguous distribution are produced by two-dimensional reconstruction. For details, please refer to Figure 1 .

[0080] Of course, in actual applications, other models of visible light drones with aerial photography functions and capable of taking high-definition images can be selected according to specific circumstances, such as: DJI Mini 4 Pro drone, DJI Air 3S, Botan ATO drone, etc., and other processing software can also be selected, such as Pix4D, ContextCapture, PhotoScan, etc., which are not limited by the present invention.

[0081] Step S2: Based on DOM and DSM, obtain land use type and terrain roughness index.

[0082] In the specific implementation process, for example: based on the DOM of a newly developed orchard with concentrated and contiguous distribution, refer to the land use classification standard of the "Technical Guide for Dynamic Monitoring of Soil and Water Loss", and use human-computer interactive visual interpretation to obtain land use type data. For details, please refer to Figure 2 Different colors represent different land use types. Based on the DSM of a newly developed orchard with concentrated and contiguous distribution, the terrain roughness index was calculated using SedInConnect software. For details, please refer to Figure 3 , different colors are used to represent the terrain roughness index value.

[0083] Among them, the terrain roughness index is a local measure of the roughness of the terrain surface, which is obtained by calculating the standard deviation of the residual terrain on a scale of several meters. The smoothed DSM is obtained by averaging the original DSM on an n×n (usually 3×3, 5×5, etc., 5×5 is taken here) unit moving window, and then the terrain roughness index is obtained by calculating the difference between the original DSM and the smoothed DSM. The calculation formula of the terrain roughness index is as follows:

[0084] ,

[0085] In the formula, RI is the terrain roughness index, n 2 is the number of pixels in the n×n moving window, x iis the elevation value of a specific pixel in the sliding window, x m n 2 The average elevation of pixels.

[0086] Step S3: Calculate the green leaf index and vegetation coverage index based on DOM.

[0087] In the specific implementation process, for example: since commonly used drones are equipped with visible light lenses, their visible light aerial images lack near-infrared bands and cannot estimate NDVI (Normalized Difference Vegetation Index). Therefore, based on the visible light drone DOM, GLI (Green Leaf Index) is used instead of NDVI to characterize the LAI (Leaf Area Index) of newly developed orchards with concentrated and contiguous distribution, and then the vegetation coverage is estimated, solving the problem of estimating vegetation coverage using visible light drones.

[0088] Traditional remote sensing technology mainly relies on the near-infrared band to calculate vegetation index and estimate vegetation coverage. However, commonly used drones are equipped with visible light lenses, and their visible light aerial images lack the near-infrared band and cannot estimate NDVI. By using the green leaf index, a vegetation index that does not rely on the near-infrared band, instead of the commonly used NDVI to estimate vegetation coverage, it can make up for the lack of near-infrared bands in visible light drone aerial images and the inability to estimate NDVI. It can expand the application scope of visible light drone remote sensing technology and make it more flexible and applicable.

[0089] Visible light drone DOM has the advantages of fast acquisition speed, wide coverage and low cost. By combining the green leaf index and advanced estimation methods, it can achieve rapid and accurate monitoring of vegetation coverage in newly developed orchards. This helps to timely understand the vegetation growth status, spatial distribution characteristics and potential soil and water loss risks in orchards, and provide a scientific basis for the refined management and development of orchards.

[0090] Specifically, the green leaf index is a vegetation index that does not rely on the near-infrared band. It uses the information of the green, red, and blue bands of the visible light band for calculation. For details, please refer to Figure 4 The green leaf index value is represented by different colors. Vegetation coverage is one of the important indicators to measure the status of surface vegetation. It indicates the proportion of vegetation coverage per unit surface area. For details, please refer to Figure 4 , different colors are used to represent vegetation coverage values.

[0091] The calculation formula of green leaf index is as follows:

[0092] ,

[0093] In the formula, GLI is the green leaf index, Red , Green , Blue They are the DN values ​​of the red, green and blue bands in DOM;

[0094] The calculation formula of vegetation coverage index is as follows:

[0095] ,

[0096] In the formula, FVC is the vegetation coverage index, To evaluate the maximum value of the green leaf index of vegetation in the area, It is the minimum green leaf index of bare soil in the assessment area.

[0097] Step S4: Generate the slope factor and slope length factor of the CSLE model based on the DSM.

[0098] In the specific implementation process, for example, based on the DSM data of a newly developed orchard with concentrated and contiguous distribution, the slope factor (abbreviated as S factor) and slope length factor (abbreviated as L factor) of the CSLE model are calculated with the help of the soil erosion model terrain factor calculation tool or the "raster calculator" tool in ArcGIS. For details, please refer to Figure 5 , using different colors to represent the S factor value and the L factor value, showing the distribution of the S factor and the L factor in the orchard.

[0099] Step S5: Obtain the coverage and biological measure factors, engineering measure factors and tillage measure factors of the CSLE model.

[0100] In the specific implementation process, for example:

[0101] (1) Coverage and biological measures factors;

[0102] Based on the vegetation coverage index of a newly developed orchard with concentrated and continuous distribution, the CSLE model B factor was calculated and generated using the segmented estimation equation of the coverage and biological measures factor (B factor for short) suitable for orchard soil and water loss estimation. For details, please refer to Figure 6 , different colors are used to represent the B factor value, and the calculation formula is as follows:

[0103] ,

[0104] In the formula, B For coverage and biological measures factors, FVC is the vegetation coverage index.

[0105] (2) Engineering measures factors;

[0106] Referring to the "Report on Dynamic Monitoring Results of Soil and Water Loss in Provincial Monitoring Areas of Jiangxi Province in 2023", the engineering measure factor (abbreviated as E factor) of the orchard plot is assigned a value of 0.151, and the E factor of other plots is assigned a value of 1 to obtain the E factor of the CSLE model. For details, please refer to Figure 6 , different colors represent different E-factor values.

[0107] (3) Farming practice factors;

[0108] Taking into account the land use type and slope in step S2, the CSLE model tillage factor (abbreviated as T factor) was generated using Table 1 applicable to newly developed orchards with concentrated and contiguous distribution. Figure 6 , different colors are used to represent different T factor values.

[0109] Table 1: T factor estimation table for newly developed orchards with concentrated and contiguous distribution

[0110]

[0111] Step S6: Use the CSLE model to estimate the soil erosion modulus and perform soil erosion intensity classification to obtain the soil erosion intensity grade.

[0112] In the specific implementation process, for example, based on the S factor and L factor of the CSLE model in step S4, and the B factor, E factor, and T factor in step S5, with the help of the rainfall erosivity factor (abbreviated as R factor) and soil erodibility factor (abbreviated as K factor) issued by the Ministry of Water Resources for dynamic monitoring of soil and water loss, the soil erosion modulus of the newly developed orchards with concentrated and continuous distribution is calculated. For details, please refer to Figure 7 , different colors are used to represent the soil erosion modulus value. The calculation formula of soil erosion modulus is as follows:

[0113] ,

[0114] Where A is the soil erosion modulus, R is the rainfall erosivity factor, K is the soil erodibility factor, L is the slope length factor, S is the slope factor, B is the cover and biological measures factor, E is the engineering measures factor, and T is the tillage measures factor.

[0115] According to the soil erosion classification and grading standard SL190-2007 of the Ministry of Water Resources, soil erosion intensity is graded based on the soil erosion modulus, as shown in Table 2.

[0116] Table 2: Soil hydraulic erosion intensity classification standard

[0117]

[0118] For the southern red soil hilly area, when the soil erosion modulus is less than 500 t / km2 When the soil erosion modulus is 500 t / km 2 ·a~2500 t / km 2 When the soil erosion modulus is 2500 t / km 2 ·a~5000 t / km 2 When the soil erosion modulus is 5000 t / km 2 ·a~8000 t / km 2 When the soil erosion modulus is 8000 t / km 2 ·a~15000 t / km 2 When the soil erosion modulus exceeds 15,000 t / km, the soil erosion intensity level is extremely strong. 2 When ·a, the soil erosion intensity level is severe erosion.

[0119] Step S7: Calculate the sediment connectivity index based on the terrain roughness index weight and the vegetation cover and management factor weight.

[0120] In the specific implementation process, for example: sediment connectivity reflects the cascade relationship between the source and sink of sediment in the basin, characterizes the difficulty of the migration and output of erosion sediment in the basin, and can be used to diagnose and quantify the sediment transport path and its temporal and spatial changes, and explore the hot spots of sediment sources. Taking into account the complex terrain and large changes in vegetation coverage of the newly developed orchards with concentrated and contiguous distribution, the terrain roughness index weight and the vegetation coverage and management factor weight of the sediment connectivity index were calculated based on the DSM and vegetation coverage of the newly developed orchards with concentrated and contiguous distribution, and the sediment connectivity index was constructed by comprehensively considering the influence of terrain and vegetation. The calculation formula is as follows:

[0121] ,

[0122] In the formula, IC is the sediment connectivity index, IC The value range is [-∞,+∞], IC The bigger it is, the more connected it is; D up and D dn They are the upslope and downslope parts of sediment connectivity. D up Indicates the potential for sediment generated upstream to migrate downward, the downslope portion D dn The main consideration is the runoff path length of sediment particles migrating to the nearest target water area or sink landscape; and are the average terrain roughness index weight and average vegetation cover and management factor weight of the upstream contribution area, is the average slope of the upstream contributing area, A is the area of ​​the upstream contribution region, d i DSM i The length of the runoff path along the steepest downslope direction for each grid, , and S i Respectively i The weights of the terrain roughness index, vegetation cover and management factor, and slope for each raster.

[0123] Based on DSM, calculate the terrain roughness index weight (RI weight for short), please refer to Figure 8 , different colors are used to represent the RI weight value. The RI weight reflects the impact of topography on sediment connectivity. The calculation formula is as follows:

[0124] ,

[0125] In the formula, is the terrain roughness index weight, RI is the terrain roughness index, The maximum value of the terrain roughness index in the assessment area;

[0126] Based on the vegetation coverage index, the vegetation coverage and management factor weights (referred to as C factor weights) are calculated. For details, please refer to Figure 8 , different colors are used to represent the C factor weight value, which reflects the impact of vegetation on sediment connectivity. The C factor weight is directly represented by the vegetation coverage and management factors of the USLE / RUSLE model, and the calculation formula is as follows:

[0127] ,

[0128] In the formula, is the weight of vegetation cover and management factor, C It is the vegetation cover and management factor.

[0129] Step S8: Calculate the sediment connectivity index and classify it to obtain the sediment connectivity grade.

[0130] In the specific implementation process, for example: based on the DSM, RI weight, and C factor weight of the newly developed orchard with concentrated and contiguous distribution, combined with the current land use status, with the help of SedInConnect software, the two reservoirs and their downstream channels in a newly developed orchard with concentrated and contiguous distribution were taken as the target objects, and the sediment connectivity index (IC for short) was used to calculate the sediment connectivity index from each part of the assessment area to the reservoir and its downstream channel. For details, please refer to Fig. 9 , different colors are used to represent the sediment connectivity index value.

[0131] Sediment connectivity levels include low connectivity, medium connectivity and high connectivity. The sediment connectivity index classification method includes:

[0132] The difference between the mean of the sediment connectivity index and the standard deviation of the sediment connectivity index was determined as the lower boundary of the sediment connectivity index;

[0133] The sum of the mean value of the sediment connectivity index and the standard deviation of the sediment connectivity index is determined as the upper boundary of the sediment connectivity index;

[0134] When the sediment connectivity index is less than the lower boundary of the sediment connectivity index, the sediment connectivity level is low connectivity; when the sediment connectivity index is between the lower boundary of the sediment connectivity index and the upper boundary of the sediment connectivity index, the sediment connectivity level is medium connectivity; when the sediment connectivity index is greater than the lower boundary of the sediment connectivity index, the sediment connectivity level is high connectivity.

[0135] Step S9: Characterizing the in-situ impact level of soil erosion based on the soil erosion intensity level in step S6, and characterizing the ex-situ impact level of soil erosion based on the sediment connectivity level in step S8.

[0136] In the specific implementation process, for example: based on the soil erosion intensity level of the newly developed orchard area with concentrated and continuous distribution, characterize the in-situ impact level of soil erosion. For details, please refer to Fig.10 , different colors are used to indicate the in-situ impact level of soil and water loss. Combined with the actual needs of soil and water loss risk assessment in newly developed orchards, the in-situ impact levels of soil and water loss include no impact, low impact, medium impact and high impact. When the soil erosion intensity level is slight erosion, the in-situ impact level of soil and water loss is no impact; when the soil erosion intensity level is mild erosion, the in-situ impact level of soil and water loss is low impact; when the soil erosion intensity level is moderate erosion, the in-situ impact level of soil and water loss is medium impact; when the soil erosion intensity level is strong erosion, extremely strong erosion or severe erosion, the in-situ impact level of soil and water loss is high impact.

[0137] The sediment connectivity level of the newly developed orchard area based on concentrated and contiguous distribution is used to characterize the level of ectopic impact of soil erosion. For details, please refer to Fig.10, different colors are used to indicate the level of soil and water loss ex situ impact. Combined with the actual needs of soil and water loss risk assessment in newly developed orchards, the levels of soil and water loss ex situ impact include low impact, medium impact and high impact. When the sediment connectivity level is low, the level of soil and water loss ex situ impact is low impact; when the sediment connectivity level is medium connectivity, the level of soil and water loss ex situ impact is medium impact; when the sediment connectivity level is high connectivity, the level of soil and water loss ex situ impact is high impact.

[0138] Step S10: Weigh the in-situ and ex-situ impacts of soil erosion and assess the risk level of soil erosion.

[0139] In the specific implementation process, for example: based on the in-situ impact level of soil erosion and the ex-situ impact level of soil erosion in the newly developed orchards with concentrated and contiguous distribution, a distribution map of the in-situ and ex-situ impact trade-off relationship of soil erosion is generated through spatial overlay analysis. For details, please refer to Fig.11 Different colors are used to indicate the trade-off between in situ and ex situ impacts of soil erosion. Combined with the actual needs of soil erosion risk assessment in newly developed orchards, the soil erosion risk is comprehensively assessed according to the corresponding relationship in the soil erosion risk level evaluation table. For details, please refer to Fig.11 Different colors are used to indicate the level of soil and water loss risk, which include no risk, low risk, medium-low risk, medium risk, medium-high risk, and high risk.

[0140] Please refer to Table 3. When the on-site impact level of soil and water loss is high impact, and the ex-situ impact level of soil and water loss is high impact or medium impact, the soil and water loss risk level is high risk; when the on-site impact level of soil and water loss is high impact, and the ex-situ impact level of soil and water loss is low impact, or when the on-situ impact level of soil and water loss is medium impact, and the ex-situ impact level of soil and water loss is high impact, the soil and water loss risk level is medium-high risk; when the on-situ impact level of soil and water loss is medium impact, and the ex-situ impact level of soil and water loss is medium impact, the soil and water loss risk level is medium risk; when When the in-situ impact level of soil and water loss is medium impact and the ex-situ impact level of soil and water loss is low impact, or when the in-situ impact level of soil and water loss is low impact and the ex-situ impact level of soil and water loss is high impact, the soil and water loss risk level is medium-low risk; when the in-situ impact level of soil and water loss is low impact and the ex-situ impact level of soil and water loss is medium impact or low impact, the soil and water loss risk level is low risk; when the in-situ impact level of soil and water loss is no impact and the ex-situ impact level of soil and water loss is high impact, medium impact or low impact, the soil and water loss risk level is no risk.

[0141] Table 3: Soil and water loss risk level evaluation table

[0142]

[0143] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for assessing soil and water loss risk in a newly developed orchard based on drone aerial photography, characterized in that: The method is applicable to newly developed orchards with concentrated and contiguous distribution, including: Obtain DOM and DSM based on visible light drone aerial images of newly developed orchards with concentrated and contiguous distribution; Based on DOM, the green leaf index is used instead of the normalized vegetation index to estimate the vegetation coverage index. The calculation formula is as follows: ， ， In the formula, GLI is the green leaf index, Red , Green , Blue They are the DN values ​​of the red, green and blue bands in DOM. FVC is the vegetation coverage index, To evaluate the maximum value of the green leaf index of vegetation in the area, The minimum green leaf index of bare soil in the assessment area; Based on the vegetation coverage index, the coverage and biological measure factors of the CSLE model were calculated using the segmented estimation equation for coverage and biological measure factors suitable for orchard soil and water loss estimation. The calculation formula is as follows: ， In the formula, B for coverage and biological measures factors; The CSLE model is used to estimate the soil erosion modulus and to classify the soil erosion intensity. The soil erosion intensity grade is used to characterize the in-situ impact grade of soil and water loss, which includes no impact, low impact, medium impact and high impact. Based on DSM, the weight of terrain roughness index is calculated; based on vegetation coverage index, the weight of vegetation coverage and management factor is calculated; the calculation formulas of terrain roughness index weight and vegetation coverage and management factor weight are as follows: ， ， In the formula, is the terrain roughness index weight, RI is the terrain roughness index, To evaluate the maximum value of the terrain roughness index in the area, is the weight of vegetation cover and management factor, C is the vegetation cover and management factor; Taking into account the influence of topography and vegetation, the sediment connectivity index is calculated, and the calculation formula is as follows: ， In the formula, IC is the sediment connectivity index, D up and D dn are the upslope and downslope parts of sediment connectivity, and are the average terrain roughness index weight and average vegetation cover and management factor weight of the upstream contribution area, is the average slope of the upstream contributing area, A is the area of ​​the upstream contribution region, d i DSM i The length of the runoff path along the steepest downslope direction for each grid, , and S i Respectively i The weight of the terrain roughness index, the weight of the vegetation cover and management factor, and the slope of each grid; Based on the sediment connectivity index, the sediment connectivity level is used to characterize the level of soil and water loss ex situ impact, which includes low impact, medium impact and high impact; Based on the in-situ impact level of soil and water loss and the ex-situ impact level of soil and water loss, the in-situ-ex-situ impact trade-off relationship of soil and water loss is generated through spatial overlay analysis, and the soil and water loss risk level is evaluated.

2. The method according to claim 1, characterized in that Based on the in-situ impact level of soil and water loss and the ex-situ impact level of soil and water loss, the in-situ-ex-situ impact trade-off relationship of soil and water loss is generated through spatial overlay analysis to assess the risk level of soil and water loss, specifically including: When the in-situ impact level of soil and water loss is high, and the ex-situ impact level of soil and water loss is high or medium, the soil and water loss risk level is high risk; When the in-situ impact level of soil and water loss is high impact and the ex-situ impact level of soil and water loss is low impact, or when the in-situ impact level of soil and water loss is medium impact and the ex-situ impact level of soil and water loss is high impact, the soil and water loss risk level is medium-high risk; When the in-situ impact level of soil and water loss is medium, and the ex-situ impact level of soil and water loss is medium, the soil and water loss risk level is medium risk; When the in-situ impact level of soil and water loss is medium impact and the ex-situ impact level of soil and water loss is low impact, or when the in-situ impact level of soil and water loss is low impact and the ex-situ impact level of soil and water loss is high impact, the soil and water loss risk level is medium-low risk; When the in-situ impact level of soil and water loss is low impact, and the ex-situ impact level of soil and water loss is medium impact or low impact, the soil and water loss risk level is low risk; When the in-situ impact level of soil and water loss is no impact, and the ex-situ impact level of soil and water loss is high impact, medium impact or low impact, the soil and water loss risk level is no risk.

3. The method according to claim 1, characterized in that The soil erosion intensity level is used to characterize the in-situ impact level of soil and water loss, specifically: When the soil erosion intensity level is slight erosion, the in-situ impact level of soil and water loss is no impact; When the soil erosion intensity level is mild erosion, the in-situ impact level of soil and water loss is low impact; When the soil erosion intensity level is moderate erosion, the in-situ impact level of soil and water loss is medium impact; When the soil erosion intensity level is strong erosion, extremely strong erosion or severe erosion, the in-situ impact level of soil and water loss is high impact.

4. The method according to claim 1, characterized in that The sediment connectivity level is used to characterize the level of out-of-situ impact of soil erosion, specifically: When the sediment connectivity level is low connectivity, the ex situ impact level of soil erosion is low impact; When the sediment connectivity level is medium connectivity, the level of soil and water loss ectopic impact is medium impact; When the sediment connectivity level is high connectivity, the ex situ impact level of soil erosion is high impact.

5. The method according to claim 4, characterized in that The classification is based on the sediment connectivity index, including: The difference between the mean of the sediment connectivity index and the standard deviation of the sediment connectivity index was determined as the lower boundary of the sediment connectivity index; The sum of the mean value of the sediment connectivity index and the standard deviation of the sediment connectivity index is determined as the upper boundary of the sediment connectivity index; When the sediment connectivity index is less than the lower boundary of the sediment connectivity index, the sediment connectivity level is low connectivity; when the sediment connectivity index is between the lower boundary of the sediment connectivity index and the upper boundary of the sediment connectivity index, the sediment connectivity level is medium connectivity; when the sediment connectivity index is greater than the lower boundary of the sediment connectivity index, the sediment connectivity level is high connectivity.

6. The method according to claim 1, characterized in that The calculation formula of the terrain roughness index is as follows: ， In the formula, RI is the terrain roughness index, n 2 is the number of pixels in the n×n sliding window, x i is the elevation value of a specific pixel in the sliding window, x m n 2 The average elevation of pixels.

7. The method according to claim 1, characterized in that The calculation formula of the soil erosion modulus is as follows: ， Where A is the soil erosion modulus, R is the rainfall erosivity factor, K is the soil erodibility factor, L is the slope length factor, S is the slope factor, B is the cover and biological measures factor, E is the engineering measures factor, and T is the tillage measures factor.

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