Land ecological quality interannual rating and change detection method

Abstract

The invention relates to the technical field of ecological change evaluation, and discloses a land ecological quality interannual rating and change detection method to solve the technical problems of complex modeling, strong parameter dependence, tedious operation and the like in a conventional change extraction method, and a land ecological quality rating scheme is constructed by using the first two principal components of tasseled cap transformation, ALBEDO and LST. The ecological quality level of the land surface is quantified, and periodic monitoring of the ecological quality condition is achieved; the method comprises the following steps: introducing a multi-time sequence dynamic rate of ecological change, quantifying a land surface ecological quality change (improvement / degradation) level, combining a semi-variance function theory, constructing a function relationship between the ecological quality dynamic rate and a corresponding observation time interval according to different time intervals (from small to large) in sequence, and obtaining a dynamic rate mean value at the same time interval; and carrying out extreme value detection on the maximum / minimum value of the dynamic rate under different time intervals in the interannual observation period to obtain a multi-time-sequence dynamic rate product data set representing the change of the land ecosystem.

Description

Technical Field

[0001] This application belongs to the field of ecological change assessment technology, specifically involving a method for interannual rating and change detection of terrestrial ecological quality. Background Technology

[0002] The evolution of terrestrial ecosystems exhibits significant temporal continuity and spatial heterogeneity, with changes often manifesting as a cumulative, slow-onset, but far-reaching dynamic process. Ecological change involves both qualitative and quantitative changes. Generally, the dynamic monitoring of surface ecosystems can be broadly categorized into three types: first, intra-annual seasonal fluctuations, reflecting the ecosystem's cyclical response to climate rhythms; second, interannual abrupt events, such as abrupt changes in vegetation cover types caused by human intervention; and third, trans-annual gradual trends, referring to the continuous adjustment of the ecosystem's degradation, restoration, or succession direction. In a narrow sense, ecological change detection primarily focuses on the latter two. Analyzing time-series remote sensing satellite data to identify the trajectory and trend patterns of ecosystem changes over longer time periods has become an important direction in ecological monitoring research.

[0003] In recent years, with the deepening of ecological civilization construction, "ecological quality assessment" has gradually become a research focus in fields such as geography, ecological remote sensing, and land resource management. Traditional change detection methods are mostly based on pixel-level difference calculations (such as NDVI difference and ratio methods), relying on low spatial resolution remote sensing data, and are mainly suitable for macro-level assessments at the national or global scale. Although these methods have played an important role in large-scale surface change identification, their calculation results are limited by problems such as large noise interference, coarse granularity of change identification, and lack of continuous analysis. With the improvement of the temporal resolution and processing capabilities of remote sensing data, change detection methods based on time series analysis have gradually become mainstream. Among them, the introduction of high-density remote sensing time series has significantly improved the timeliness and accuracy of ecological change information extraction. However, most existing methods still focus on single-point change identification over a fixed time span, lacking a systematic characterization of the changing patterns of "interannual dynamic rates" at different time scales.

[0004] Classification-based Continuous Change Detection (CCDC) is a high-time-series ecological change detection method. It simulates seasonal and trend changes in land cover by fitting sinusoidal and linear data to multi-band time series of pixels. A change is considered to have occurred when continuous observations deviate from the model's predicted values ​​by more than three times the root mean square error. CCDC can simultaneously identify change points and classify land cover, making it suitable for large-scale continuous monitoring. Its advantages include sensitive change response and intuitive classification results; however, it is sensitive to data quality and model parameters, susceptible to noise interference from clouds and other sources, prone to "spurious changes," and has high computational resource requirements.

[0005] Temporal Convolutional Networks (TCNs), which combine image time series analysis with deep learning techniques, are increasingly being introduced into the field of ecological change detection. Compared with traditional methods, TCNs offer advantages such as end-to-end modeling capabilities, no need for manual feature extraction, and the ability to handle nonlinear changes, making them particularly suitable for the analysis of long-term, multi-source remote sensing data. However, their model training process requires significant amounts of data and computational resources, and they possess a certain "black box" nature, affecting the interpretability of the model results and hindering practical applications in ecological causal analysis and policy support.

[0006] In summary, although current remote sensing methods for detecting ecological changes have made significant progress in terms of accuracy and timeliness, they generally suffer from problems such as complex modeling, strong parameter dependence, and cumbersome operation, making them difficult to adapt to the actual needs of multi-regional and multi-scale ecological monitoring. In particular, many methods suffer from slow response and inaccurate time positioning in identifying quantitative processes and periodic fluctuations at interannual scales, which restricts the reliability of ecological monitoring results and their decision-making reference value. Summary of the Invention

[0007] This application provides a method for interannual rating and change detection of terrestrial ecological quality to solve the technical problems of conventional change extraction methods, such as complex modeling, strong parameter dependence, and cumbersome operation.

[0008] This invention provides a method for interannual assessment and change detection of terrestrial ecological quality. First, the method constructs an interannual ecological quality evaluation system using the two principal components before tasseled cap transformation, surface albedo (ALBEDO), and surface temperature (LST) to quantitatively assess the annual ecological status within the observation period. Second, it constructs interannual ecological changes over different time spans (e.g., 1 year, 2 years, ... N years). Extreme value detection identifies the maximum / minimum values ​​of the dynamic rate at different time intervals within the interannual observation period, obtaining the average dynamic rate across multiple time series representing ecosystem changes, and quantitatively measuring the dynamic changes in the annual surface ecological quality value within the observation period.

[0009] A method for interannual assessment and change detection of terrestrial ecological quality includes the following steps: S1, Acquire satellite imagery data during the growing season: S1.1, from April to October each year, acquire high-temporal-series remote sensing images, including at least blue, green, red, near-infrared and thermal infrared bands.

[0010] S2, Ecological Factor Extraction: S2.1, using the remote sensing ENVI platform, the acquired visible light band, near-infrared band and thermal infrared band are used to calculate and obtain surface vegetation information, soil moisture information, surface temperature information and surface reflectance, so as to realize data preprocessing; S2.2, complete the normalization processing of surface temperature information and surface reflectivity; S2.3, repeat S2 to obtain a multi-time series annual value dataset for each ecological factor.

[0011] S3, Calculation results of the annual value of the comprehensive terrestrial surface ecological quality index. S3.1, using water body masks to remove the impact of large areas of water on land; S3.2, the weight allocation is obtained through principal component analysis to complete the calculation of the comprehensive ecological index; S3.3 enables regional cropping and ecological grading of the comprehensive ecological index to obtain the annual value results of the terrestrial surface ecological quality assessment. S3.4 Repeat step 3 to obtain the annual value data set of terrestrial surface ecological quality assessment.

[0012] S4 Extreme Value Detection Multi-Year Dynamic Rate Mean S4.1 Calculate the annual value of the terrestrial surface ecological quality assessment, and the maximum / minimum value of the dynamic rate at different time intervals within the extreme value detection observation period; S4.2, Analyze the changing trends of ecological quality; S4.3 enables the saving of the maximum / minimum values ​​of the average dynamic rate per pixel.

[0013] Acquisition and rating of multi-year ecological change information in the S5 study area S5.1, Determine whether the calculation is complete based on the required number of years. S5.2, If the calculation is completed, compare and analyze the final experimental data with the terrestrial ecological change detection accuracy analysis table; S5.3, generate detection result map based on the verification points of the final experimental data and ecological changes; S5.4 Project the final experimental data; the projection should be consistent with the original data projection. S5.5 saves data and enables the extraction of dynamic change information of terrestrial ecosystems.

[0014] Due to the adoption of the above technical solution, the beneficial effects achieved by this application are as follows: 1. By acquiring high-temporal-series remote sensing images from April to October each year, this method can continuously track the ecological change process throughout the growing season, overcoming the limitation that relying solely on single-point images is insufficient to reflect dynamic evolution, thereby achieving long-term temporal-series monitoring of the spatiotemporal evolution of ecological quality. 2. Using the ArcGIS platform, Python algorithm programs were written to perform normalization and missing value filling, building a seamless and automated image processing workflow. This significantly reduced the workload of manual correction and post-processing data integration, and improved processing efficiency and stability. 3. Principal component analysis automatically and scientifically allocates the weights of factors such as soil moisture, vegetation cover, surface temperature and surface reflectivity, eliminating subjective weighting values ​​and enhancing the ability of the comprehensive ecological quality index to objectively reflect multi-source ecological information in the region. 4. During the calculation process, a large-area water body mask was specially introduced and clipped in combination with the vector boundary of the study area, which effectively eliminated the interference of water bodies and edge areas, making the terrestrial ecological quality assessment more focused on the target surface, avoiding misjudgment, and improving the accuracy of ecological rating and change detection; 5. By performing dynamic rate calculation and extreme value detection on the interannual ecological index series, this method, combined with trend analysis of the annual ecological quality dataset, automatically identifies the peak and valley nodes of ecological quality changes, enabling accurate capture of ecological mutations or rapid recovery periods, and providing reliable support for early warning. 6. By analyzing the dynamic rate, the rate of change of the surface ecology over multiple time spans is quantified; by extracting the extreme values ​​of the dynamic rate, the most significant improvement or degradation process of the surface ecological quality each year is identified. Attached Figure Description

[0015] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 A technical flowchart for the interannual rating of terrestrial ecological quality; Figure 2 A graph showing the results of the interannual assessment of terrestrial ecological quality; Figure 3 A diagram analyzing the trend of terrestrial ecological change; Figure 4 This is a schematic diagram of the extreme value detection algorithm for multi-time series interannual dynamic rate; Figure 5 This is a diagram showing the verification points for experimental data and the results of ecological change monitoring. Detailed Implementation

[0016] To more clearly illustrate the overall concept of this application, a detailed explanation is provided below with reference to the accompanying drawings.

[0017] Many specific details are set forth in the following description to provide a thorough understanding of this application. However, this application may also be implemented in other ways different from those described herein. Therefore, the scope of protection of this application is not limited to the specific embodiments disclosed below. It should be noted that, unless otherwise specified, the embodiments of this application and the features thereof can be combined with each other.

[0018] The method described in this embodiment is for constructing an ecological environment quality index from multi-temporal remote sensing image data. It mainly includes steps such as data acquisition, preprocessing, feature extraction, index calculation, interpolation synthesis, and ecological scoring. The entire process can be implemented in a geospatial analysis platform.

[0019] S1, Collect remote sensing image data within the growing season range, the remote sensing image data shall include at least the visible light band, near-infrared band and thermal infrared band; In this embodiment, remote sensing image data is acquired through an analysis platform. Images of the study area during the vegetation growing season are selected for analysis, and the time range can be set according to requirements. The spatial resolution of the selected images is 30 meters. Considering spectral requirements, at least the visible light band, near-infrared band, and thermal infrared band should be included for vegetation and humidity index calculation; and the thermal infrared band should be included for calculating land surface temperature (LST). To ensure data quality, images with low cloud cover should be selected first, and images from the same or similar dates should be used as much as possible to reduce seasonal and observation angle differences.

[0020] Preferably, the visible light band includes the blue band, green band, and red band, which has physical significance and signal-to-noise ratio advantages in vegetation monitoring and surface reflectance feature extraction.

[0021] First, the blue band is extremely sensitive to scattering from water, clouds, fog, and the atmosphere, and can be used for cloud detection and water masking; in addition, in soil and sparse vegetation areas, blue light reflects strongly, which can help distinguish between vegetation and bare land.

[0022] Secondly, vegetation in the green band absorbs less light and reflects more light, which is a good indicator of vegetation health. When combined with red light and near-infrared light, commonly used vegetation indices can be constructed to reflect chlorophyll content and photosynthetic activity.

[0023] Third, the red band is absorbed in large quantities by plant leaves, which can form a sharp contrast with the near-infrared band and be used to extract biomass, leaf area index, etc.; the combination of red light and near-infrared light is also the core input of the "Greenness" component in the tassel cap transformation.

[0024] Preferably, the growing season is selected from June to October each year. This period is usually the peak growth period for vegetation in temperate and subtropical regions, with the largest leaf area and the strongest photosynthetic activity, which can significantly reflect the health of the ecosystem. At the same time, it avoids the low vegetation cover before spring sprouting and the abrupt change in reflection during the autumn leaf fall period, ensuring comparability and seasonal consistency between different years. The multi-temporal images acquired within 4 months are sufficient to construct robust annual values, while also taking into account the amount of data and processing costs.

[0025] Preferably, cloud cover should be <10% during the growing season. Clouds and cloud shadows can cause reflectivity distortion, interfere with tassel transformation and temperature inversion, and introduce erroneous values. Limiting cloud cover to below 10% can maximize the retention of clear pixels and reduce the impact of subsequent interpolation and noise. In large-scale automated processing, this threshold provides a good balance between data availability and quality.

[0026] As shown in Table 1, vegetation information and soil moisture information were extracted from the remote sensing image data of S1 based on the tassel transform algorithm. At the same time, surface temperature information and surface reflectance were obtained based on the remote sensing model inversion method, and the surface temperature information and surface reflectance were normalized. Table 1. Satellite Imagery Product Collection Table.

[0027] The specific processing procedure is as follows: S2.1, band calculations obtain surface vegetation information (Greenness, the first component of the tasseled cap transform), soil moisture information (Wetness, the second component of the tasseled cap transform), surface temperature information (LST), and surface reflectance (Albedo), representing surface ecological productivity, soil moisture content, surface temperature, and surface exposure, respectively. The calculation results need to be checked for outliers and interpolated. The main filtering algorithm is: Con(IsNull("raster"),FocalStatistics("raster", NbrRectangle(5, 5, "CELL"),"MEAN"),"raster").

[0028] S2.2 Normalizes the ecological factors, performs dimensionless processing, and achieves comparability between different indicators. Analyze the value range of terrestrial ecological factors during the growing season to determine the normalization range (0, 1) of each ecological indicator, and finally achieve normalization.

[0029] S2.3, Repeat step 2 to obtain a multi-time series annual value dataset for each ecological factor.

[0030] S3, as Figure 1 As shown, water body masking is applied to the annual values ​​of ecological factors to remove interference from water bodies in the ecological assessment. Specifically, the Normalized Difference Water Index (NDWI) is used to distinguish between water bodies and land. Pixels with an NDWI ≥ 0.27 are identified as water bodies, and these pixels are masked. Simultaneously, large water bodies with an area greater than 10 km² are excluded to avoid the influence of water area on the terrestrial ecological quality assessment results. After the above preprocessing, a surface reflectance image sequence after radiometric and atmospheric correction is obtained for subsequent feature extraction.

[0031] S3.1, by using a water body mask, a large area (10 km²) of land-based water is removed, eliminating the impact of water bodies on the ecological quality assessment results and improving the accuracy of ecological change detection. Implementation statement: outRas = Con(outNDWI>= 0.27, 0,Con(outNDWI<0.27, 1, 0)).

[0032] S3.2 uses principal component analysis to obtain weight allocation and complete the calculation of the comprehensive ecological index, replacing manual assignment and removing the influence of subjectivity on the evaluation results.

[0033] S3.3, regional cropping and ecological classification of the comprehensive ecological index to obtain the annual value results of terrestrial ecological quality assessment, cropping the obtained comprehensive ecological index to the study area, mapping the comprehensive ecological index value to a percentage system of 0 to 100, and dividing the comprehensive ecological index into 5 levels of "poor", "fairly poor", "medium", "good" and "excellent" according to the evenly divided interval, thus obtaining the ecological quality rating of each pixel every year.

[0034] S3.4 Repeat the above steps to obtain the terrestrial surface ecological quality assessment results for each year in the study area, forming a continuous interannual ecological quality data set.

[0035] S4, such as Figure 1 as well as Figure 2 As shown, the mean dynamic rate of each pixel in the study area is calculated at different time intervals.

[0036] It should be noted that, in Figure 2 (a), (b), and (c) represent the ecological quality assessment results of the study area in 2019, 2022, and 2023, respectively.

[0037] S4.1 Based on the annual terrestrial ecological quality rating results, a model is built on the ERDAS platform to calculate the maximum interannual dynamic rate; like Figure 3 As shown, solid dots represent the time points (T) when remote sensing data is acquired. The maximum interannual dynamic rate is calculated on datasets with time intervals ∆t, 2∆t, 3∆t...h∆t (the value of h depends on the actual amount of data used for calculation) using the following formula: QUOTE (1) S max = MAX (S 1Δt , S 2Δt , S 3Δt ,……S hΔt ) (k xi >0) (2) Smin = MIN (S 1Δt , S 2Δt , S 3Δt ,……S hΔt ) (k xi <0) (3) In the formula, ∆t is the time interval, that is, the time distance between one observation and another; X (i) Let S be the i-th observation; N is the number of observations in the time interval h ∆t, and S is the ith observation. hΔt This represents the mean dynamic rate over a time interval Δt. Dynamic rate (S) max Or S min It can effectively and quickly determine the scope and direction of ecological change, and quantitatively measure the significance of ecological change from year to year.

[0038] S4.2, use the least squares method to fit the trend and obtain the trend coefficient k. If k > 0, it indicates that the overall trend of pixels over time is upward, then take S... max This represents the maximum interannual dynamic rate of the pixel; if k < 0, indicating a downward trend, then S is taken. min This serves as the minimum interannual dynamic rate of that pixel; The least squares method is used to estimate trend values, and the equation calculation formula is as follows: (4).

[0039] S4.3, such as Figure 4 As shown, the extreme values ​​of the mean dynamic rate of each pixel are saved to reflect the rate and direction of changes in the surface ecological quality.

[0040] S5: Acquisition and rating of multi-year ecological change information in the study area.

[0041] S5.1 Determine whether the interannual dynamic rate extraction has covered the predetermined observation period. If the year requirement is met, the calculation ends.

[0042] S5.2, as shown in Table 2, after the calculation is completed, the final experimental data will be compared and analyzed with the terrestrial ecological change detection accuracy analysis table; Table 2. Analysis of the accuracy of surface ecological change detection based on experimental data.

[0043] S5.3, such as Figure 5 As shown, the detection result map is generated by verifying the final experimental data and analyzing ecological changes.

[0044] S5.4. The final interannual ecological quality data and dynamic rate extreme value data are uniformly projected to ensure consistency with the projection coordinate system of the original remote sensing data.

[0045] S5.5 Save Data: Save all processing results as raster data format to extract and visualize information on the dynamic changes of the terrestrial ecosystem in the study area.

[0046] For any parts not mentioned in this application, existing technologies may be used or referenced.

[0047] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0048] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for detecting annual rating and changes of terrestrial ecological quality, characterized in that , comprising the following steps: S1, collecting remote sensing image data within the range of the growing season, the remote sensing image data containing at least visible light bands, near-infrared bands and thermal infrared bands; Wherein, the growing season is from April to October every year, and the cloud cover within the time range of the growing season should be <10%; Wherein, the visible light bands include blue bands, green bands and red bands; S2, extracting vegetation information and soil moisture information based on the remote sensing image data of S1 using the Canny transform, obtaining surface temperature information and surface reflectivity based on a remote sensing inversion model, and normalizing the surface temperature information and the surface reflectivity; S3, performing water body mask on the normalized annual value data of each ecological factor to obtain an annual value data set of the surface ecological quality evaluation result of the monitoring area; S4, detecting the mean value of the multi-year dynamic rate of the annual value data of the land ecological quality evaluation; S5, combining the results of S3 and S4 to generate a multi-time dynamic rate product data set, and saving the maximum / minimum dynamic rate and the corresponding interval information for each image to realize the extraction of the interannual rating and change information of the land ecological quality.

2. The method of claim 1, wherein, In step S2, the following steps are included: S2.1, calculating to obtain ecological factors such as surface vegetation information, soil moisture information, surface temperature information and surface reflectivity based on visible light bands, near-infrared bands and thermal infrared bands; Wherein, the calculation result is subjected to outlier detection and interpolation filling through a filtering algorithm: Con(IsNull("raster"), FocalStatistics("raster", NbrRectangle(5, 5, "CELL"), "MEAN"), "raster") to realize data preprocessing; S2.2, normalizing the ecological factors to complete the de-dimensioning processing; S2.3, repeating S2 to obtain a multi-time annual value data set of each ecological factor.

3. The method of claim 1, wherein, In step S3, the following steps are included: S3.1, removing large-area water areas on land through water body mask; S3.2, obtaining weight distribution through principal component analysis to complete comprehensive ecological index calculation; S3.3, regionally clipping and ecologically grading the comprehensive ecological index to obtain the annual value result of the land ecological quality evaluation; S3.4, repeating S3 to obtain an annual value data set of the surface ecological quality evaluation result of the monitoring area.

4. The method of claim 1, wherein, In step S4, the following steps are included: S4.1, calculating the interannual result of the land ecological quality evaluation, and detecting the maximum / minimum value of the dynamic rate at different time intervals within the observation period; S4.2, according to the least square method to estimate the trend value, if the trend coefficient k > 0, the dynamic rate takes S max , calculate the maximum value; if the trend coefficient k < 0, the dynamic rate takes S min , calculate the minimum value; S4.3, saving the maximum / minimum value of the pixel-by-pixel dynamic rate mean value.

5. The method of claim 1, wherein, In step S5, the following steps are included: S5.1, judging whether the calculation is completed according to the required years for calculation; S5.2, if the calculation is completed, comparing and analyzing the final experimental data with the land ecological change detection precision analysis table; S5.3, generating a detection result map through the validation points of the final experimental data and the ecological change; S5.4, projecting the final experimental data; Wherein, the projection should be consistent with the original data projection; S5.5, saving the data to realize the extraction of the dynamic change information of the land ecological system.

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