A method for identifying pasture growth period based on satellite remote sensing image

Abstract

The present application relates to the technical field of pasture development period identification, and particularly relates to a pasture development period identification method based on satellite remote sensing images. The method comprises the following steps: acquiring multispectral satellite remote sensing image data of a target pasture area, extracting a canopy reflection difference value based on a near-infrared and red light band, and extracting a canopy water content index from a short-wave infrared band; calculating a ratio change amount of the canopy reflection difference value relative to the canopy water content index, and determining a consistency degree of canopy expansion and water consumption change to generate pasture growth coordination data; calculating a pigment change amount according to the pasture growth coordination data and the canopy reflection difference value, and comparing the pigment change amount with the pasture growth coordination data to obtain a structure change relative pigment change offset amount; the present application identifies the pasture development period to enhance the recognition ability of the process of pasture expansion from the outside to internal material accumulation, and reduce the interpretation deviation of a single reflection index in the dense canopy stage.

Description

Technical Field

[0001] This invention relates to the field of forage development stage identification technology, and in particular to a method for identifying forage development stages based on satellite remote sensing imagery. Background Technology

[0002] As an important natural forage resource, the developmental stage of forage grass directly affects its yield and utilization value. Therefore, accurate identification of forage grass growth processes is of great significance. Current technologies often employ vegetation indices or single spectral features based on satellite remote sensing imagery to analyze forage grass growth, such as using the Normalized Difference Vegetation Index (NDVI) and Red Edge Index (REDI) to reflect changes in vegetation cover. However, these methods primarily focus on the external spectral response of the canopy, making it difficult to distinguish the relationship between canopy structural changes and internal moisture and physiological processes. As forage grass enters the mid-to-late growth stages, the canopy gradually densifies, and the spectral signal tends to saturate, leading to a weakening of the differences between different developmental stages and thus affecting identification accuracy. Furthermore, existing methods typically neglect the coupling relationship between canopy moisture changes and structural expansion, making it difficult to characterize the dynamic characteristics of forage grass transitioning from rapid growth to material accumulation. This results in unclear boundaries in developmental stage delineation and unstable stage transition identification. Summary of the Invention

[0003] Therefore, it is necessary to provide a method for identifying the developmental stage of pasture based on satellite remote sensing imagery to solve at least one of the aforementioned technical problems.

[0004] To achieve the above objectives, a method for identifying the developmental stage of pasture based on satellite remote sensing imagery includes the following steps:

[0005] Step S1: Acquire multispectral satellite remote sensing image data of the target pastoral area, extract the canopy reflectance difference based on the near-infrared and red bands, and extract the canopy water content index from the short-wave infrared band;

[0006] Step S2: Calculate the change in the ratio of canopy reflectance difference to canopy water content index, determine the consistency between canopy expansion and water consumption changes, and generate pasture growth synergy data;

[0007] Step S3: Calculate the amount of pigment change based on the forage growth synergy data and the canopy reflectance difference, and compare the amount of pigment change with the forage growth synergy data to obtain the offset of structural change relative to pigment change; determine the forage stage change data based on the offset and the amount of pigment change.

[0008] Step S4: Perform continuous analysis on the data of changes in pasture stages, and combine it with the data on the synergy of pasture growth to determine the pasture development stage, and output the results of the pasture development stage identification.

[0009] This invention introduces a collaborative calculation mechanism between canopy reflectance difference and canopy water content, enabling forage development stage identification to no longer rely solely on single spectral features. Instead, it simultaneously reflects the coupling relationship between canopy structure changes and water consumption, thereby enhancing the ability to characterize the true state of forage growth. Step S2 constructs ratio changes and forms forage growth synergy data, allowing the canopy expansion process and water change process to be correlated and expressed on the same quantitative basis. This more stably reflects the transition characteristics of forage from rapid growth to structural stability.

[0010] By comparing the changes in pigmentation with growth synergy data, the offset of structural changes relative to spectral response is extracted, allowing the asynchronous characteristics between canopy reflectance changes and internal physiological changes to be explicitly expressed. This enhances the ability to identify the process of pasture transitioning from external expansion to internal material accumulation and reduces the interpretation bias of a single reflectance index in the dense canopy stage.

[0011] By analyzing the continuity of stage change data and combining the synchronous characteristics of structural and moisture changes in growth synergy data, key turning points in the development process can be located, making the division of the rapid growth stage, maturity stage, and decline stage of forage grass more continuous and with clearer boundaries. Overall, this invention can improve the stability of forage grass development stage identification and the consistency of stage division, and is suitable for forage grass growth monitoring and dynamic assessment under large-scale remote sensing conditions. Attached Figure Description

[0012] Figure 1 This is a flowchart illustrating the steps of a method for identifying the developmental stage of pasture based on satellite remote sensing imagery.

[0013] Figure 2 Multispectral satellite remote sensing image of the target pastoral area;

[0014] Figure 3 A schematic diagram illustrating remote sensing identification of pasture development stages;

[0015] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0016] The technical method of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0017] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0018] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0019] To achieve the above objectives, please refer to Figures 1 to 3 A method for identifying the developmental stage of pasture based on satellite remote sensing imagery includes the following steps:

[0020] All specific values ​​involved in this embodiment are exemplary parameters used to clearly illustrate the technical operation process and are not the only limitation of the present invention.

[0021] Step S1: Acquire multispectral satellite remote sensing image data of the target pastoral area, extract the canopy reflectance difference based on the near-infrared and red bands, and extract the canopy water content index from the short-wave infrared band;

[0022] Step S2: Calculate the change in the ratio of canopy reflectance difference to canopy water content index, determine the consistency between canopy expansion and water consumption changes, and generate pasture growth synergy data;

[0023] Step S3: Calculate the amount of pigment change based on the forage growth synergy data and the canopy reflectance difference, and compare the amount of pigment change with the forage growth synergy data to obtain the offset of structural change relative to pigment change; determine the forage stage change data based on the offset and the amount of pigment change.

[0024] Step S4: Perform continuous analysis on the data of changes in pasture stages, and combine it with the data on the synergy of pasture growth to determine the pasture development stage, and output the results of the pasture development stage identification.

[0025] In one embodiment, after acquiring multispectral satellite remote sensing image data of the target pasture area, the canopy reflectance difference is calculated from the near-infrared and red light bands respectively to characterize the structural density of the pasture canopy. At the same time, the canopy water content index is extracted from the short-wave infrared band to characterize the water content of the leaves. The above two types of data form a one-to-one correspondence at the same sampling location, serving as the input basis for subsequent joint analysis.

[0026] The ratio of the canopy reflectance difference to the canopy water content index at the corresponding location is calculated to obtain the ratio change that reflects the relative relationship between structural changes and water changes. Furthermore, the ratio changes at adjacent locations are compared spatially to determine whether structural expansion and water consumption change in a consistent manner. The results corresponding to the degree of consistency are used as forage growth synergy data output to define whether the forage is currently in a structure-dominated or water-dominated state.

[0027] Under the constraint of forage growth synergy data, the canopy reflectance difference is corrected and calculated to weaken the influence of structural expansion on the reflectance difference, and the amount of pigment change is obtained. Then, the amount of pigment change is compared with the forage growth synergy data, and the offset of structural change relative to pigment change is determined by the difference between the two change magnitudes. Based on the combination relationship between the offset and the amount of pigment change, forage stage change data is generated.

[0028] Extract continuous variation relationships from pasture stage change data, and combine pasture growth synergy data to locate key positions of structural expansion and moisture changes. At these positions, determine the transition state of leaves from continuous expansion to overlapping and stabilization, or from structure-dominated to material accumulation-dominated, and then divide the pasture development stages to output pasture development period identification results.

[0029] In another embodiment, multispectral images of the corresponding time period are acquired in step S1, and the canopy reflectance difference and canopy water content index are calculated. In the early stage of greening, the canopy reflectance difference is low and the water content index is high. As growth progresses, the canopy reflectance difference gradually increases, reflecting the leaf unfolding and enhanced coverage.

[0030] The ratio of the reflection difference to the water content index at each location was calculated. In the early stage of growth, the change in the ratio was significant as the reflection difference increased, indicating that the structural expansion was consistent with the change in water content. However, in the middle and late stages, the reflection difference continued to increase while the change in water content index weakened in some areas, and the corresponding forage growth coordination data showed a decrease in consistency.

[0031] The difference in canopy reflectance in the above-mentioned areas was corrected to obtain the amount of pigment change. In areas where structural expansion tends to be stable, the amount of pigment change still changes. This difference is then used to form a difference with the data on the coordination of pasture growth, and the offset of structural change lagging behind pigment change is obtained. Based on this, it is determined that the area has entered the stage transition process.

[0032] By combining the regions where the offset is stable with the variation characteristics in the forage growth synergy data, the range of areas where the leaves change from rapid expansion to overlapping and stabilization and gradually turn to material accumulation is determined. This allows for the division of forage into rapid growth stage, maturity stage, and subsequent stage, thus completing the developmental stage identification.

[0033] Please refer to [link / reference needed] for further information. Figure 2 The target pastoral area clearly marked by the red box in the image is a remote sensing satellite image area for identifying the pasture development stage by acquiring multispectral data and analyzing near-infrared / red band reflectance differences and short-wave infrared water content indicators.

[0034] Please refer to [link / reference needed] for further information. Figure 3 The division of the rapid growth stage, maturity stage and decline stage in the figure is a visual recognition conclusion transformed from the pasture stage change data obtained by a series of collaborative analyses, such as canopy reflectance difference, pigment change amount and structure-pigment shift amount.

[0035] Of particular importance, step S2 includes:

[0036] Canopy reflectance difference and canopy water content index are extracted within the same canopy area, and the ratio between the two is calculated to obtain the ratio distribution at each sampling location;

[0037] Based on the ratio distribution, the change in ratio between adjacent locations is calculated, and the synchronous change segment of canopy reflection difference and canopy water content index is extracted according to the continuous change of ratio.

[0038] Within the synchronous change zone, the change range of the canopy reflectance difference and the change range of the canopy water content index are compared and calculated to determine the consistency of the change direction and the degree of consistency between the two, and the calculation results corresponding to the degree of consistency are used as the synergistic data of pasture growth.

[0039] In one embodiment, within the same canopy region, the canopy reflectance difference and the canopy water content index are extracted based on their location correspondence, so that the two form a one-to-one matching relationship at the same sampling location. On this basis, the ratio of the canopy reflectance difference to the canopy water content index is calculated for each sampling location, and the ratio distribution covering the entire canopy region is obtained, which is used to uniformly characterize the relative relationship between structural changes and water changes.

[0040] After obtaining the ratio distribution, the difference of the ratio at each sampling location is calculated along the spatial adjacency relationship to obtain the ratio change between adjacent locations; in the continuously distributed ratio change, the region with the same direction of change is extracted as the segment where the canopy reflectance difference and the canopy water content index change synchronously, thus transforming the original ratio result into a continuous change feature.

[0041] Within the synchronous change zone, the variation range of canopy reflectance difference and canopy water content index are calculated separately, and the two are compared to determine whether the direction of change is consistent and whether the variation range is of the same order of magnitude. Based on this, the corresponding results of the direction of change and the variation range are uniformly quantified to obtain the degree of consistency between canopy expansion and water consumption. This degree of consistency is used as the output of pasture growth synergy data to provide constraints for the subsequent calculation of pigment change.

[0042] In another embodiment, taking the transition of forage grass from its early growth stage to its rapid growth stage in a pastoral area as an example, multiple adjacent sampling locations are selected within the same canopy area, and the corresponding canopy reflectance difference and canopy water content are extracted respectively. In the early growth stage, the canopy reflectance difference at each location is low, and the water content is high, with the ratio distribution obtained by ratio calculation generally in a low range. As the forage grass leaves gradually unfold, the ratio difference between adjacent locations gradually increases. By calculating the change in ratio between adjacent locations, a continuous area is identified where the ratio shows an increasing trend. This area is the segment where structural expansion and water change occur simultaneously.

[0043] Within this section, the variation ranges of canopy reflectance difference and canopy water content were further calculated. When both changed in the same direction and the variation ranges remained within a similar range, it could be determined that structural expansion and water consumption had a high degree of consistency. When some areas showed significant changes in reflectance difference but small changes in water content, the corresponding degree of consistency decreased. Based on the above comparison results, the degree of consistency in each area was quantified to obtain forage growth coordination data. Areas with a high degree of consistency corresponded to the rapid leaf unfolding stage, while areas with a decreased degree of consistency corresponded to subsequent changes in growth status, providing a basis for subsequent stage division.

[0044] Of particular importance is the extraction of canopy reflectance difference and canopy water content index within the same canopy region, and the calculation of their ratio, yielding the ratio distribution at each sampling location, including:

[0045] Within the same canopy region, the canopy reflectance difference and canopy water content index of adjacent sampling locations are selected, and a location correspondence is established to obtain the original values ​​of each sampling location.

[0046] The ratio of the canopy reflectance difference to the canopy water content index at each sampling location was calculated, and the range of values ​​for the canopy reflectance difference and the canopy water content index was limited during the calculation process to obtain a stable ratio result;

[0047] Based on the ratio results at each sampling location, a spatially continuous distribution is constructed according to the ratio differences between adjacent locations, forming a ratio distribution that reflects the coupling state between changes in canopy structure and water content.

[0048] In one embodiment, within the same canopy area, spatially adjacent sampling locations are selected, and the canopy reflectance difference and canopy water content index at each location are extracted accordingly, so that each location simultaneously possesses structural change information and water content change information, and a one-to-one correspondence is established to form a unified data foundation.

[0049] After obtaining the correspondence, the ratio is calculated for each sampling location. The ratio result is obtained with the canopy reflection difference as the numerator and the canopy water content index as the denominator. During the calculation process, the range of the values ​​of the canopy reflection difference and the canopy water content index is limited, and abnormal values ​​or data outside the effective range are eliminated to keep the ratio results obtained at each location within the comparable range.

[0050] After obtaining the ratio results of each sampling location, the ratio difference between adjacent locations is calculated based on the spatial adjacency relationship, and the adjacent locations are arranged according to the connection order of adjacent locations, so that the discrete ratio results are transformed into a distribution structure with spatial continuity. In this distribution structure, the ratio change at each location reflects the correspondence between the changes in canopy structure and water content, thus forming a ratio distribution covering the entire canopy area.

[0051] In another embodiment, taking the process of pasture distribution changing from sparse distribution to enhanced coverage as an example, multiple adjacent sampling locations are selected within the same canopy area, for example, several points are selected sequentially along the direction of pasture rows, the canopy reflectance difference and canopy water content index of each point are obtained, and the correspondence between the points is established.

[0052] Based on this, the ratios at each location were calculated. In the initial stage, due to the limited coverage of grass leaves, the canopy reflectance difference was low while the water content was high, resulting in an overall low ratio. As the leaves gradually unfolded, the reflectance difference at some locations increased, while the water content changed little, and the corresponding ratios began to increase.

[0053] Furthermore, by calculating the ratio differences between adjacent points, it was observed that the ratios at some consecutive points showed a gradually increasing trend, while other areas showed relatively small changes. Connecting these differences according to spatial location reveals a continuously distributed ratio structure, where areas with continuously increasing ratios correspond to locations with significant canopy structure expansion, and areas with slower ratio changes correspond to locations with relatively stable structural changes. Through this process, the ratio calculation results at a single point are extended into a spatially correlated ratio distribution, used to characterize the spatial coupling state between canopy structure changes and water content changes.

[0054] Preferably, step S3 includes:

[0055] Based on the data on the synergy of pasture growth, the canopy reflectance difference was divided into high-value areas and low-value areas, and the variation of canopy reflectance difference within each area was calculated based on adjacent sampling locations.

[0056] Based on the variation of canopy reflectance difference, the sections where structural expansion slows down are determined, and the canopy reflectance difference within each section is corrected to obtain the amount of pigment change;

[0057] By correlating the changes in pigment with the data on the synergistic effect of pasture growth, and calculating the difference between the magnitude of the change in pigment and the magnitude of the change in the synergistic effect of pasture growth, the offset direction of structural change relative to pigment change is obtained.

[0058] The difference is divided into intervals based on the direction of offset, the offset magnitude is extracted, and the data on changes in pasture stages are determined.

[0059] In one embodiment, the canopy reflectance difference is spatially divided based on the forage growth synergy data. Areas in the forage canopy with high consistency between structural changes and moisture changes are classified as high-value areas, while areas with low consistency or a tendency to separate are classified as low-value areas, so that different growth states can form spatially distinguishable treatment units.

[0060] Within each zone, canopy reflectance differences are extracted along adjacent sampling locations, and the variation amplitude between adjacent locations is calculated, transforming the canopy reflectance difference from a discrete sampling form into a spatially continuous variation. This variation amplitude reflects the transition process of pasture leaves from expanding and increasing to stable cover in different regions. After obtaining the variation amplitude, based on the spatial distribution characteristics of the variation amplitude, segments where the canopy reflectance difference changes from continuous enhancement to limited expansion are identified. These segments are defined as the process interval of slowed structural expansion, and the canopy reflectance difference is consistently corrected within this interval to maintain a correspondence with the structural change state, thereby obtaining the pigment change amount.

[0061] After the pigment change data is generated, it is correlated with the forage growth coordination data within the same spatial region, and the change amplitudes of both are calculated separately. By calculating the difference between the change amplitudes, the offset direction of structural change relative to pigment change is obtained, which is used to characterize the sequential or differential relationship between structural and pigment changes. After the offset direction is determined, the difference is segmented, and the interval structure is divided according to the consistency of the offset direction in different intervals. The offset amplitude in the corresponding interval is extracted, thus forming forage stage change data that can reflect the relationship between forage structure change and pigment change.

[0062] In another embodiment, taking the transition of forage from rapid growth to a stable growth period as an example, in the same pastoral area, the canopy area is divided according to the forage growth coordination data. The area where the leaves expand rapidly is characterized by a continuous increase in the canopy reflectance difference and a synchronous increase in moisture change, and is divided into a high-value area, while the area where the leaves tend to be stable or where moisture dominates the change is divided into a low-value area.

[0063] In the high-value area, the difference in canopy reflection was extracted along adjacent sampling points. It was observed that the change in the difference between adjacent positions was relatively continuous, reflecting that the canopy structure gradually became denser during the leaf expansion process. In the low-value area, the change between adjacent positions weakened, reflecting that the canopy tended to be in a stable coverage state.

[0064] Based on this, the variation range of canopy reflectance difference was analyzed. When it was found that some areas changed from continuous enhancement to growth limitation, these areas were identified as structural expansion slowdown sections. The reflectance difference in these sections was uniformly corrected so that it could reflect the pigment response characteristics of pasture in the process of transitioning from rapid expansion to stable cover.

[0065] Subsequently, the corrected pigment change was compared with the forage growth synergy data. The change amplitude of the two was calculated separately in the same spatial area to identify the sequential difference between structural change and pigment change, such as the case where structural change precedes or lags behind pigment change. Based on this difference, the difference was divided into intervals to obtain the distribution of the offset amplitude corresponding to different stages, thereby forming stage change data that can distinguish different developmental stages of forage, which can be used for subsequent developmental period division.

[0066] Preferably, the canopy reflectance difference is partitioned based on the forage growth synergy data, dividing it into high-value and low-value zones, and the variation range of the canopy reflectance difference within each zone is calculated based on adjacent sampling locations, including:

[0067] Based on the data on the synergy of pasture growth, the rate of structural change is extracted. When the rate of structural change is greater than a preset rate threshold, the difference in canopy reflection at the corresponding location is obtained.

[0068] The reflection intervals are divided according to the continuous change of the canopy reflection difference. The interval where the canopy reflection difference continues to increase is defined as the high value area, and the interval where the change slows down is defined as the low value area.

[0069] The increment of the difference in canopy reflectance between adjacent locations is calculated within the high-value region to obtain the magnitude of the change in canopy reflectance from sparse to dense.

[0070] The variation range of canopy reflectance difference is calculated in the low-value region to obtain the variation range when the canopy tends to a stable state.

[0071] In one embodiment, the spatial distribution of canopy reflectance difference is screened based on pasture growth synergy data, and structural change rate parameters are extracted from locations where structural changes and moisture changes are highly coupled. When the structural change rate exceeds a preset rate threshold, the canopy reflectance difference corresponding to that location is determined to participate in subsequent partitioning processing, thereby preferentially extracting areas with significant structural changes from the overall canopy.

[0072] After obtaining the target canopy reflection difference, the canopy reflection difference is divided into intervals according to the continuous spatial variation relationship. The continuous segment where the canopy reflection difference continues to increase is classified as the high value area, and the continuous segment where the growth of the canopy reflection difference tends to be slow or the change amplitude weakens is classified as the low value area, so that different growth intensity states are spatially structured and separated.

[0073] In the high-value area, the incremental change of the canopy reflectance difference is calculated along the adjacent sampling positions. The incremental change is used to characterize the process of the evolution of the grass leaves from the initial sparse distribution to the spatial dense coverage. The change corresponding to this process is taken as the change range of the canopy from sparse to dense.

[0074] In the low-value area, the variation of canopy reflectance difference is also calculated along adjacent sampling locations, but the focus is on characterizing the attenuation process of the variation. This attenuation reflects the trend of the pasture canopy transitioning from rapid expansion to a stable coverage state, thus obtaining the variation of the canopy tending to a stable state.

[0075] Preferably, calculating the increment of the canopy reflectance difference between adjacent locations within the high-value region to obtain the magnitude of the canopy transition from sparse to dense includes:

[0076] Within the high-value area of ​​the canopy, the distribution of canopy reflectance difference between adjacent sampling locations is determined, and the spatial adjacency relationship between adjacent locations is determined based on the distribution of canopy reflectance difference.

[0077] The increment of canopy reflectance difference is calculated based on spatial adjacency, and the leaf cover change in the corresponding area is obtained;

[0078] In areas where the increment of canopy reflectance difference continues to increase and the change in leaf cover intensifies, identify the structural change segments corresponding to the transition of leaves from dispersed to overlapping;

[0079] Within the structural change zone, the magnitude of the canopy transition from sparse to dense is determined based on the increment of the canopy reflection difference.

[0080] In one embodiment, the canopy reflectance difference of adjacent sampling locations is selected in the high-value area, and these locations are spatially distributed to make the relative positional relationship of each sampling point in the canopy clear. On this basis, the spatial adjacency relationship between adjacent locations is constructed to characterize the spatial connection state of the pasture canopy structure.

[0081] After establishing spatial adjacency relationships, the canopy reflectance difference increment is calculated based on the correspondence between adjacent positions, transforming the canopy reflectance difference from discrete sampled values ​​into an incremental sequence reflecting spatial change trends. Simultaneously, by combining the changes in the canopy reflectance difference increment, information on the changes in leaf cover status within the corresponding area is obtained, which is used to reflect the evolution of leaves from initial distribution to cover enhancement.

[0082] After obtaining the incremental changes and leaf cover changes, the spatial synchronization relationship between the two is analyzed. When the incremental change in canopy reflectance difference continues to increase and the leaf cover increases synchronously, the region is identified as being in a structural change segment where the leaves are transitioning from dispersed to overlapping. This further refines the structural evolution process from the overall high-value area. Within the structural change segment, the incremental change in canopy reflectance difference is further accumulated along spatial adjacency relationships. The accumulated incremental results are used to characterize the intensity change of the canopy as it gradually evolves from a sparse to a dense structure, thus obtaining the magnitude of the canopy's transition from sparse to dense.

[0083] In another embodiment, taking the example of pasture grass increasing in coverage from the initial sparse distribution to the middle stage, sampling points of adjacent plots are selected in the high value area, and the connection relationship of adjacent sampling points is determined according to the spatial relationship between plots, so that it can reflect the continuous distribution state of pasture grass on the actual ground surface.

[0084] Based on this, the increment of the canopy reflection difference between adjacent sampling points was calculated. It was observed that in the area where the leaves gradually unfold, the increment between adjacent points gradually increases, and the leaf coverage area in the corresponding area also gradually expands, showing a transition from scattered distribution to local overlap.

[0085] When this incremental increase and enhanced leaf cover occur continuously in the same area, the area can be identified as a structural change zone, that is, a transitional region where the pasture canopy is evolving from a sparse structure to a dense structure.

[0086] Within this structural change zone, by accumulating and comparing the incremental difference in canopy reflection, the degree of densification in different regions can be further distinguished. For example, some regions change rapidly, corresponding to rapid leaf overlap, while some regions change slowly, corresponding to gradual structural stabilization, thus forming the distribution of the range of change in the canopy from sparse to dense.

[0087] Preferably, calculating the canopy reflectance difference increment based on spatial adjacency relationships and obtaining the leaf cover change in the corresponding area includes:

[0088] Under spatial adjacency, the difference in canopy reflection at adjacent locations is selected, and the increment of the difference between adjacent locations is calculated to form a local reflection difference distribution;

[0089] The continuity of the local reflection difference distribution is determined, the region where the difference increment changes from discrete to continuous is identified, and the canopy connection state between adjacent locations changes from discrete to continuous distribution.

[0090] Under canopy connectivity, the degree of superposition of canopy reflection difference increments at adjacent locations is statistically analyzed to identify regions where multiple locations show simultaneous enhancement of difference increments;

[0091] The change in leaf cover as the blades transition from dispersed to overlapping is determined based on the superimposed enhanced regions.

[0092] In one embodiment, the canopy reflection difference between adjacent sampling locations is selected under the constraint of spatial adjacency, so that there is a clear spatial correspondence between adjacent locations. On this basis, the difference between the canopy reflection difference between adjacent locations is calculated to obtain the difference increment reflecting the local spatial change, and the increment results of each location are organized into a local reflection difference distribution, thereby transforming the single-point reflection information into a spatial correlation change structure.

[0093] After forming a local reflection difference distribution, the continuity of the spatial arrangement of the difference increment is determined, the region where the difference increment transitions from a discrete distribution to a continuous distribution is identified, and the spatial structure corresponding to the transition region is determined as the canopy connection state, so that the originally scattered sampling points form a continuous correlation at the structural level.

[0094] Based on the canopy connectivity, the incremental differences in canopy reflection at adjacent locations are statistically superimposed to obtain spatial clusters of simultaneous enhancement at multiple locations, which can be used to reflect the synchronous change characteristics of the canopy structure in a local area.

[0095] After obtaining the superimposed enhanced region, its corresponding spatial range is matched with the blade coverage state to determine the blade coverage change from dispersed distribution to overlapping coverage, so that the blade structure change can be characterized by the spatial superposition feature of the reflection difference increment.

[0096] Preferably, the sections where structural expansion slows down are determined based on the magnitude of the change in canopy reflectance difference, and the canopy reflectance difference within each section is corrected to obtain the amount of pigment change, including:

[0097] A time series curve was constructed based on the variation of the canopy reflectance difference. Trend analysis was performed on the time series curve to extract the segment where the variation changed from continuous increase to decrease. The segment where the variation approached zero was identified as the segment where the structural expansion slowed down.

[0098] The canopy was divided into sections where structural expansion slowed down, and the correspondence between the change in reflection difference and the change in blade cover was extracted to determine the structural state sections where the blades transitioned from overlapping enhancement to stable arrangement.

[0099] Within the structural state segment, when the rate of change of the change amplitude decreases to a preset threshold range, the transition process from continuous density to stable density is determined, and the range of change amplitude of the reflection difference is corrected accordingly to obtain the amount of pigment change.

[0100] In one embodiment, the spatial variation of the pasture canopy is processed into a time series based on the variation of the canopy reflectance difference. The variation amplitudes at different sampling locations are organized according to the observation order to form a time series curve, which is used to reflect the overall trend of canopy structure change as it grows.

[0101] After the time series curve is generated, trend analysis is performed on the curve to identify the segment where the change amplitude gradually changes from continuous increase to a decrease in the increase amplitude. The segment where the change amplitude gradually approaches a stable state is further screened and identified as the segment where the structural expansion slows down. This makes the transition process of pasture from rapid expansion to stable growth clearly separated at the curve level.

[0102] Within the structural expansion slowdown zone, the canopy is divided into zones, and the correspondence between the variation in canopy reflection difference and the variation in leaf cover is extracted in each zone. This is used to describe the structural change process of the leaves gradually transitioning from overlapping enhancement to stable arrangement, thus forming structural state zones.

[0103] Within the structural state segment, when the rate of change of the change amplitude decreases to a preset threshold range, the state is determined as a transition process from continuous densification to stable densification. Based on this, the change amplitude of the original canopy reflectance difference is range-corrected so that it can correspond to the change characteristics under stable growth conditions, thereby obtaining the amount of pigment change.

[0104] Preferably, the amount of pigment change is correlated with the forage growth synergy data, and the difference between the magnitude of the pigment change and the magnitude of the forage growth synergy data is calculated to obtain the offset direction of structural change relative to pigment change, including:

[0105] Establish the correspondence between pigment change and forage growth synergy data within the same canopy area, and obtain the variation range of pigment change and forage growth synergy data within that area;

[0106] The difference was calculated based on the magnitude of the change to obtain the difference distribution of pigment change relative to the forage growth synergy data, and the segment where the difference changed from positive to negative was identified.

[0107] Extract the changing state of the canopy reflectance difference within this section and identify the areas where the canopy reflectance difference changes from increasing to decreasing;

[0108] Within this region, based on the correspondence between the direction of the difference change and the state of the canopy reflection difference change, the offset direction of the structural changes relative to the pigment changes during the transition of leaves from dispersed cover to overlapping cover is determined.

[0109] In one embodiment, within the same canopy area, the amount of pigment change and the data on the synergy of pasture growth are correlated one-to-one according to the spatial sampling location to form a positional comparison relationship, and the change amplitude of the two at the corresponding locations is extracted respectively. Based on this, the difference of the change amplitude of adjacent sampling locations is calculated to obtain a difference sequence unfolding along the canopy distribution direction, and this difference sequence is used as the basis for comparing structural changes and pigment changes. Further, the direction of the difference sequence is determined, and when the difference continuously transitions from a positive value to a negative value, the canopy area corresponding to the transition is determined. The change state of the canopy reflection difference is introduced in this area, and the interval from continuous increase to limited growth is marked. Finally, the change of the difference direction is correlated with the change state of the canopy reflection difference to determine the offset direction of the structural change of the leaves relative to the pigment change during the transition from dispersed cover to overlapping cover.

[0110] In another embodiment, taking a certain pasture canopy sample area as an example, in the early stage of growth, the change in pigment and the growth synergy data are basically synchronized, and the difference is close to zero. As the leaves gradually overlap, the pigment change in local areas increases faster than the structural change, and the corresponding difference is positively distributed. When the canopy densification stage is entered, the structural change is enhanced but the pigment change slows down, and the difference gradually turns negative. In the transition zone where the difference turns from positive to negative, the canopy reflectance difference is observed to change from continuous increase to limited increase. Thus, this section is identified as the key area for the transformation of leaves from scattered coverage to overlapping coverage, and the offset direction is determined accordingly.

[0111] Preferably, step S4 includes:

[0112] Locate the transition zone in the data on changes in pasture stages, where continuous changes give way to slow changes;

[0113] Within the transition zone, the data on the synergy of pasture growth and the difference in canopy reflection were correlated to determine the location where canopy expansion slowed down and the location where water content changes changed.

[0114] Within the overlapping area where the canopy expansion slows down and the water content change inflection point, the developmental state segment corresponding to the transition of leaves from spatial expansion to nutrient accumulation is determined, and the stage boundary is extracted.

[0115] Based on the connection relationship of the stage boundaries, the transformation process of forage grass from the rapid growth stage to the mature stage and the decline stage is divided, and the results of forage grass development stage identification are output.

[0116] In one embodiment, a stage sequence relationship is constructed in the forage stage change data. By analyzing the continuity of the change amplitude of adjacent stages, the interval from continuous change to a decrease in the change amplitude is identified and designated as a transition segment. Within this transition segment, the forage growth coordination data and the canopy reflectance difference are mapped to correspond spatially, establishing a regional constraint relationship between structural expansion information and canopy spectral changes, and determining the location where canopy expansion slows down and the location where water content changes turn. Furthermore, within the spatially overlapping area of ​​the two locations, the state segment where the leaves shift from being dominated by spatial expansion to being dominated by internal nutrient accumulation is extracted, and stage boundaries are formed accordingly. Finally, based on the spatial connection relationship of each stage boundary, the evolution path of forage from the rapid growth stage to the mature stage and the decline stage is divided, and the corresponding developmental stage identification results are output.

[0117] In another embodiment, taking a continuously growing forage plot as an example, in the early stage, the canopy reflectivity increases and water consumption progresses simultaneously, and the stage change data shows a continuous upward trend. When entering the middle and late stages of growth, the structural expansion rate decreases, and at the same time, the water content index shows an inflection point. Correspondingly, the location where the canopy expansion slows down and the location where the water content changes change are marked in the same spatial area. When the two overlap in a local area, the change characteristics of the leaves from mainly expanding externally to mainly accumulating internal substances can be observed. This area is used as the stage boundary. Based on the spatial arrangement of multiple boundaries, the forage growth process is divided into three stages: rapid growth, maturity, and decline, and the final developmental stage identification result is output.

[0118] Preferably, within the overlapping area of ​​the location where canopy expansion slows down and the location of the water content change transition, the developmental state segment corresponding to the transition of leaves from spatial expansion to nutrient accumulation is determined, and the stage boundaries are extracted, including:

[0119] Determine the canopy coverage area within the overlapping region where canopy expansion slows down and water content changes inflection point;

[0120] The correlation between the canopy reflectance difference and pigment change was extracted within the canopy coverage area to identify the region where the canopy reflectance difference tends to be stable and the pigment change continues to change.

[0121] Within the region where the change in canopy reflectance difference tends to be stable and the change in pigment amount continues to change, the state of leaf transition from outward expansion to internal material accumulation is extracted.

[0122] Based on the continuous spatial distribution range of this state, the boundary position of the transition from growth-dominant to accumulation-dominant is determined as the stage boundary.

[0123] In one embodiment, within the spatially overlapping area of ​​the canopy expansion slowdown location and the water content change inflection point, the corresponding canopy coverage area is determined using this overlapping area as a boundary constraint, and the subsequent analysis objects are limited to this area only. Within this canopy coverage area, the correspondence between the canopy reflectance difference and pigment change is extracted according to the sampling location, and based on the spatial synchronicity of the two, areas where the canopy reflectance difference change tends to be stable while the pigment change continues to change are screened out. Within this area, through the correlation analysis of the canopy reflectance difference and leaf structure change, the state segment of the leaf from outward expansion growth to internal material accumulation is extracted. Finally, based on the continuous spatial distribution range of this state segment, the boundary position of the change from growth dominance to accumulation dominance is identified, and this position is determined as the stage boundary.

[0124] In another embodiment, taking a sample area where the forage canopy transitions from rapid growth to a stable phase as an example, a spatially overlapping area is formed at the location where canopy expansion slows down and water content changes at the same time. Within this area, the canopy coverage is basically stable. Within this coverage area, the change in reflectance difference no longer increases significantly, but the pigment index continues to change, indicating that there is still a material transformation process inside. At this time, the leaves have gradually shifted from being dominated by external expansion to being dominated by internal nutrient accumulation. When this state occurs continuously in space, a clear dividing line is formed, which corresponds to the stage boundary of the forage transitioning from growth-dominated to accumulation-dominated.

[0125] Preferably, based on the connection relationship of the stage boundaries, the transition process of forage grass from the rapid growth stage to the maturity stage and the decline stage is divided, and the output forage grass development stage identification results include:

[0126] The connection sequence of adjacent boundaries is determined based on the spatial distribution relationship of the stage boundaries, thus forming the stage division path;

[0127] On the stage division path, the combination state of the canopy reflectance difference and pigment change in each stage is determined to distinguish the different states of leaves, which are mainly characterized by rapid expansion and mainly characterized by material accumulation.

[0128] In regions where material accumulation is dominant and water content changes tend to be stable, the transition zone from a high-activity state to a low-activity state of the leaves is identified.

[0129] Based on the changing segments, the system is divided into rapid growth stage, maturity stage, and decline stage, and the developmental stage identification results are output.

[0130] In one embodiment, based on the spatial distribution relationship of the boundaries of each stage, the connection order of adjacent boundaries is determined according to the proximity and continuity between the boundaries, thereby forming a stage division path that runs through the pasture growth process. On this stage division path, the combination state of the canopy reflectance difference and pigment change in the corresponding area is extracted segment by segment according to the path node, and the difference in the combination state is used to determine whether the leaf is in a developmental state dominated by rapid external expansion or internal material accumulation. Furthermore, in the continuous area where the combination state shows that material accumulation is dominant and the water content change tends to be stable, the change segment of the leaf from high growth activity to low growth activity is identified. Finally, based on the positional relationship of the change segment in the stage division path, the pasture development process is divided into a rapid growth stage, a maturity stage, and a decline stage, and the corresponding developmental stage identification results are output.

[0131] In another embodiment, taking the same pasture sample area as an example, the boundaries of different stages are spatially connected. By connecting these boundaries in order of position, a growth path from early to late stages can be formed. In the early part of the path, the canopy reflectance is significantly enhanced and the pigment changes rapidly, corresponding to the rapid expansion of leaves. In the middle part of the path, the pigment changes continue but the canopy expansion weakens, indicating that the stage is dominated by material accumulation. In the later part of the path, when the water content changes tend to stabilize and material accumulation dominates, a continuous area of ​​decreased leaf activity can be observed. Based on the distribution of this continuous area in the path, the overall process is divided into three stages: rapid growth, maturity, and decline, and the developmental stage identification results are output.

[0132] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A method for identifying the developmental stage of pasture based on satellite remote sensing imagery, characterized in that, Includes the following steps: Step S1: Acquire multispectral satellite remote sensing image data of the target pastoral area, extract the canopy reflectance difference based on the near-infrared and red bands, and extract the canopy water content index from the short-wave infrared band; Step S2: Calculate the change in the ratio of canopy reflectance difference to canopy water content index, determine the consistency between canopy expansion and water consumption changes, and generate pasture growth synergy data; Step S3: Calculate the amount of pigment change based on the forage growth synergy data and the canopy reflectance difference, and compare the amount of pigment change with the forage growth synergy data to obtain the offset of structural change relative to pigment change; determine the forage stage change data based on the offset and the amount of pigment change. Step S4: Perform continuous analysis on the data of changes in pasture stages, and combine it with the data on the synergy of pasture growth to determine the pasture development stage, and output the results of the pasture development stage identification.

2. The method for identifying the pasture development stage based on satellite remote sensing imagery according to claim 1, characterized in that, Step S3 includes: Based on the data on the synergy of pasture growth, the canopy reflectance difference was divided into high-value areas and low-value areas, and the variation of canopy reflectance difference within each area was calculated based on adjacent sampling locations. Based on the variation of canopy reflectance difference, the sections where structural expansion slows down are determined, and the canopy reflectance difference within each section is corrected to obtain the amount of pigment change; By correlating the changes in pigment with the data on the synergistic effect of pasture growth, and calculating the difference between the magnitude of the change in pigment and the magnitude of the change in the synergistic effect of pasture growth, the offset direction of structural change relative to pigment change is obtained. The difference is divided into intervals based on the direction of offset, the offset magnitude is extracted, and the data on changes in pasture stages are determined.

3. The method for identifying the developmental stage of pasture based on satellite remote sensing imagery according to claim 2, characterized in that, Based on the data on the synergy of pasture growth, the canopy reflectance difference was divided into high-value and low-value zones. Within each zone, the variation range of the canopy reflectance difference was calculated based on adjacent sampling locations, including: Based on the data on the synergy of pasture growth, the rate of structural change is extracted. When the rate of structural change is greater than a preset rate threshold, the difference in canopy reflection at the corresponding location is obtained. The reflection intervals are divided according to the continuous change of the canopy reflection difference. The interval where the canopy reflection difference continues to increase is defined as the high value area, and the interval where the change slows down is defined as the low value area. The increment of the difference in canopy reflectance between adjacent locations is calculated within the high-value region to obtain the magnitude of the change in canopy from sparse to dense. The variation range of the canopy reflectance difference is calculated in the low-value region to obtain the variation range when the canopy tends to a stable state.

4. The method for identifying the developmental stage of pasture based on satellite remote sensing imagery according to claim 3, characterized in that, Within the high-value region, the increment of the canopy reflectance difference at adjacent locations is calculated, revealing the magnitude of the canopy transition from sparse to dense. Within the high-value area of ​​the canopy, the distribution of canopy reflectance difference between adjacent sampling locations is determined, and the spatial adjacency relationship between adjacent locations is determined based on the distribution of canopy reflectance difference. The increment of canopy reflectance difference is calculated based on spatial adjacency, and the leaf cover change in the corresponding area is obtained; In areas where the increment of canopy reflectance difference continues to increase and the change in leaf cover intensifies, identify the structural change segments corresponding to the transition of leaves from dispersed to overlapping; Within the structural change zone, the magnitude of the canopy transition from sparse to dense is determined based on the increment of the canopy reflection difference.

5. The method for identifying the developmental stage of pasture based on satellite remote sensing imagery according to claim 4, characterized in that, The increment of canopy reflectance difference is calculated based on spatial adjacency relationships, and the changes in leaf cover in the corresponding areas are obtained, including: Under spatial adjacency, the difference in canopy reflection at adjacent locations is selected, and the increment of the difference between adjacent locations is calculated to form a local reflection difference distribution; The continuity of the local reflection difference distribution is determined, the region where the difference increment changes from discrete to continuous is identified, and the canopy connection state between adjacent locations changes from discrete to continuous distribution. Under canopy connectivity, the degree of superposition of canopy reflection difference increments at adjacent locations is statistically analyzed to identify regions where multiple locations show simultaneous enhancement of difference increments; The change in leaf cover as the blades transition from dispersed to overlapping is determined based on the superimposed enhanced regions.

6. The method for identifying the developmental stage of pasture based on satellite remote sensing imagery according to claim 2, characterized in that, Based on the variation in canopy reflectance difference, the sections where structural expansion slows down are determined, and the canopy reflectance difference within each section is corrected to obtain the pigment variation, including: A time series curve was constructed based on the variation of the canopy reflectance difference. Trend analysis was performed on the time series curve to extract the segment where the variation changed from continuous increase to decrease. The segment where the variation approached zero was identified as the segment where the structural expansion slowed down. The canopy was divided into sections where structural expansion slowed down, and the correspondence between the change in reflection difference and the change in blade cover was extracted to determine the structural state sections where the blades transitioned from overlapping enhancement to stable arrangement. Within the structural state segment, when the rate of change of the change amplitude decreases to a preset threshold range, the transition process from continuous density to stable density is determined, and the range of change amplitude of the reflection difference is corrected accordingly to obtain the amount of pigment change.

7. The method for identifying the developmental stage of pasture based on satellite remote sensing imagery according to claim 2, characterized in that, By correlating pigment changes with forage growth synergy data and calculating the difference between the magnitude of pigment changes and the magnitude of changes in forage growth synergy data, the direction of structural change relative to pigment change is obtained, including: Establish the correspondence between pigment change and forage growth synergy data within the same canopy area, and obtain the variation range of pigment change and forage growth synergy data within that area; The difference was calculated based on the magnitude of the change to obtain the difference distribution of pigment change relative to the forage growth synergy data, and the segment where the difference changed from positive to negative was identified. Extract the changing state of the canopy reflectance difference within this section and identify the areas where the canopy reflectance difference changes from increasing to decreasing; Within this region, based on the correspondence between the direction of the difference change and the state of the canopy reflection difference change, the offset direction of the structural changes relative to the pigment changes during the transition of leaves from dispersed cover to overlapping cover is determined.

8. The method for identifying the developmental stage of pasture based on satellite remote sensing imagery according to claim 1, characterized in that, Step S4 includes: Locate the transition zone in the pasture stage change data where continuous changes become slow changes; Within the transition zone, the data on the synergy of pasture growth and the difference in canopy reflection were correlated to determine the location where canopy expansion slowed down and the location where water content changes changed. Within the overlapping area where the canopy expansion slows down and the water content change inflection point, the developmental state segment corresponding to the transition of leaves from spatial expansion to nutrient accumulation is determined, and the stage boundary is extracted. Based on the connection relationship of the stage boundaries, the transformation process of forage grass from the rapid growth stage to the mature stage and the decline stage is divided, and the results of forage grass development stage identification are output.

9. The method for identifying the developmental stage of pasture based on satellite remote sensing imagery according to claim 8, characterized in that, Within the overlapping area where canopy expansion slows down and water content changes inflection points, the developmental state segment corresponding to the transition from spatial expansion to nutrient accumulation of leaves is identified, and the stage boundaries are extracted, including: Determine the canopy coverage area within the overlapping region where canopy expansion slows down and water content changes inflection point; The correlation between the canopy reflectance difference and pigment change was extracted within the canopy coverage area to identify the region where the canopy reflectance difference tends to be stable and the pigment change continues to change. Within the region where the change in canopy reflectance difference tends to be stable and the change in pigment amount continues to change, the state of leaf transition from outward expansion to internal material accumulation is extracted. Based on the continuous spatial distribution of this state, the boundary position of the transition from growth-dominant to accumulation-dominant is determined as the stage boundary.

10. The method for identifying the developmental stage of pasture based on satellite remote sensing imagery according to claim 8, characterized in that, Based on the transitional relationships between developmental stages, the process of forage grass transitioning from the rapid growth stage to the maturity and decline stages is divided, and the output forage grass development stage identification results include: The connection sequence of adjacent boundaries is determined based on the spatial distribution relationship of the stage boundaries, thus forming the stage division path; On the stage division path, the combination state of the canopy reflectance difference and pigment change in each stage is determined to distinguish the different states of leaves, which are mainly characterized by rapid expansion and mainly characterized by material accumulation. In regions where material accumulation is dominant and water content changes tend to be stable, the transition zone from a high-activity state to a low-activity state of the leaves is identified. Based on the changing segments, the system is divided into rapid growth stage, maturity stage, and decline stage, and the developmental stage identification results are output.

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