A remote sensing estimation method of ecological carrying capacity

Abstract

This invention relates to the field of remote sensing technology and discloses a remote sensing estimation method for ecological carrying capacity. The method includes: constructing a standard spatiotemporal benchmark for multi-source data fusion; using surface temperature as a nonlinear correction factor to adjust the gain of nighttime light and calculating a human activity pressure index; calculating the neighborhood thermal potential difference based on the thermodynamic gradient principle and converting it into a thermal radiation inhibition coefficient; combining water deficit and thermal radiation inhibition to correct and normalize potential productivity, obtaining a standardized effective ecological supply index; constructing a time-series recursive model with a missing compensation mechanism to calculate ecological fatigue; and finally, comprehensively calculating the ecological carrying capacity state by integrating the supply index, pressure index, and ecological fatigue. This invention solves the problems of nighttime light saturation overflow, inconsistent supply and demand dimensions, and lack of consideration for historical cumulative effects, achieving a dynamic and refined assessment of regional ecological carrying capacity.

Description

Technical Field

[0001] This invention relates to the field of remote sensing technology, specifically to a remote sensing estimation method for ecological carrying capacity. Background Technology

[0002] With the accelerating pace of urbanization, regional ecosystems are facing increasing pressure from human activities. Accurately assessing ecological carrying capacity is crucial for optimizing national spatial planning and implementing ecological protection red line management. Remote sensing technology, with its advantages of large-scale, multi-temporal, and periodic observation, can quickly acquire information on surface vegetation growth and traces of human activities, and has become a major technical means for monitoring the balance of regional ecological supply and demand and assessing ecological security patterns.

[0003] Existing methods for estimating ecological carrying capacity based on remote sensing technology typically employ supply-demand balance models. On the supply side, net primary productivity is generally calculated using meteorological data, vegetation indices, and solar radiation data based on light energy utilization models, serving as a quantitative representation of the natural services provided by the ecosystem. On the demand side, the intensity of human activities is typically quantified using radiance values ​​from nighttime light data or population density grids spatialized from statistical data. By calculating the ratio or difference between ecological supply and human activity demand, it is determined whether the region is in a state of ecological surplus or ecological deficit.

[0004] While existing technologies have established a basic assessment framework, several shortcomings remain: First, commonly used nighttime light sensors have radiation detection thresholds. In urban core commercial areas or high-energy-consuming industrial zones, light radiation brightness data exhibits a saturation overflow effect, causing high-load areas of varying intensities to appear numerically homogenized. This makes it difficult for models to effectively distinguish between extreme and ordinary high-pressure areas, leading to an underestimation of the actual pressure in the core area. Second, existing supply-side calculations primarily focus on the vertical impact of water and heat exchange on vegetation, often neglecting the horizontal neighborhood heat radiation from the urban heat island effect on the stomatal conductance and photosynthesis of central pixel vegetation. Furthermore, the biophysical quantities calculated on the supply side and the relative indices on the demand side have fundamentally different physical dimensions, making direct algebraic difference or ratio calculations lack a rigorous mathematical and physical foundation. Finally, existing models are mostly static snapshot-style assessments based on single-phase data, lacking a memory mechanism for the historical state of the ecosystem. They cannot reflect the reduction effect of long-term accumulated ecological fatigue or historical debt on current carrying capacity, resulting in inaccurate assessments of the actual vulnerability of continuously stressed areas. Summary of the Invention

[0005] This invention aims to address the technical problems in existing ecological carrying capacity assessments, such as inconsistent spatiotemporal benchmarks for multi-source data, insufficient consideration of thermal environmental stress mechanisms, and lack of consideration for the historical cumulative effects of ecosystems.

[0006] This invention provides a remote sensing estimation method for ecological carrying capacity, which includes the following steps: S1. Establish a standard geographic raster space, resample and synthesize multi-source data to generate a spatiotemporally consistent basic dataset of vegetation index, surface temperature and nighttime light. S2 uses surface temperature as a nonlinear correction factor to adjust the gain of nighttime light data and calculates the human activity stress index. S3 calculates the weighted average thermal potential difference within the neighborhood window based on the thermodynamic gradient principle and converts it into a thermal radiation suppression coefficient. S4. The potential net primary productivity is corrected by combining water deficit and the heat radiation inhibition coefficient, and the regional theoretical maximum value is introduced for normalization to obtain the standardized effective ecological supply index. S5. Construct a time-series recursive model. Within each time step, update the current ecological fatigue level based on the historical ecological fatigue level and the current supply-demand difference, according to the data validity. S6. Calculate the ecological carrying capacity status index by combining the standardized effective ecological supply index, human activity pressure index, and ecological fatigue index.

[0007] Preferably, in S1, the resampling and timing synthesis specifically includes: A standard geographic raster space is defined. For surface temperature and vegetation index data, a bicubic convolution interpolation algorithm is used for resampling to maintain the smoothness of the surface gradient. For nighttime light data, a nearest neighbor interpolation algorithm is used for resampling to preserve the texture boundaries of the original radiance values. For vegetation index data, the maximum value synthesis method is used to generate monthly data; for surface temperature data, the effective value average synthesis method based on quality control weights is used. Specifically, after parsing the quality control file to remove cloud and shadow pixels, the effective pixels within the window period are averaged to eliminate instantaneous thermal anomaly fluctuations.

[0008] Preferably, in S2, the human activity stress index is calculated as follows: Range standardization was performed on nighttime light data and surface temperature data respectively. A temperature gain coefficient is constructed using an S-shaped nonlinear function, and the standardized nighttime light data is multiplied by the temperature gain coefficient. The temperature gain coefficient is composed of a base value of 1 and a temperature correction term, and the temperature correction term increases in an S-shaped curve as the standardized surface temperature increases. The human activity pressure index controls the upper limit of the correction range by setting a thermal environment regulation weight coefficient, and ensures that a positive gain is generated only when the temperature exceeds the regional average level by setting a center offset.

[0009] Preferably, the parameters in the S-shaped nonlinear function are set as follows: The thermal environment adjustment weighting coefficient ranges from 0.3 to 0.5 and is used to control the correction magnitude of the surface temperature on the final pressure index. The slope factor of the S-shaped function ranges from 10 to 20 and is used to control the model's sensitivity to temperature changes. The center offset is set to 0.5 as the inflection point threshold for the temperature response.

[0010] Preferably, in S3, the calculation of the weighted average thermal potential difference specifically includes: Define a square sliding window centered on the target pixel, and calculate the positive temperature difference between each neighboring pixel and the central pixel within the window; The reciprocal of the distance weight is introduced as a weighting factor to perform a weighted summation of the positive temperature difference. The result is then divided by the sum of all weights within the window and the sum of the infinitesimal constants to obtain the average thermal potential difference with scale invariance. The thermal radiation suppression coefficient is obtained by performing a negative exponential decay transformation on the average thermal potential difference, which includes a thermal sensitivity decay constant, the value of which ranges from 2.0 to 5.0.

[0011] Preferably, in S4, the calculation of the standardized effective ecological supply index specifically includes: A feature space is constructed based on surface temperature and vegetation index. Dry edge equation and wet edge equation are fitted to calculate the temperature-vegetation drought index of the pixel to characterize water deficit. The potential net primary productivity is multiplied by the water deficit correction term and the thermal stress correction term to obtain the corrected productivity; wherein the water deficit correction term is 1 minus the temperature vegetation drought index, and the thermal stress correction term is 1 minus the thermal radiation inhibition coefficient. Dividing the corrected productivity by the historical maximum potential productivity reference value within the study area yields a standardized effective ecological supply index with a value range mapped to 0 to 1.

[0012] Preferably, in S5, the ecological fatigue degree is defined as the cumulative amount of historical supply and demand imbalances in the ecosystem, and its recursive update process specifically includes: Determine the validity of the data at the current moment; If the data is valid, calculate the difference between the human activity pressure index and the standardized effective ecological supply index; if the difference is positive, add the difference to the ecological fatigue level of the previous moment after the recovery coefficient decay. If the data is invalid, determine whether the duration of data loss is within the maximum tolerance threshold. If it is within the threshold, apply exponential decay based on the natural decay constant to the ecological fatigue level of the previous time step. If it exceeds the threshold, stop decaying and maintain the state of the previous time step.

[0013] Preferably, the parameters involved in the recursive update process are set as follows: When calculating the supply-demand difference, a supply-demand balance adjustment coefficient is introduced to weight the standardized effective ecological supply index. The value of this coefficient ranges from 0.8 to 1.2. The recovery coefficient ranges from 0.1 to 0.2 and is used to characterize the self-repairing ability of an ecosystem under surplus conditions. The natural decay constant ranges from 0.05 to 0.1 and is used to characterize the natural decay process of pressure during data loss.

[0014] Preferably, in S6, the ecological carrying capacity state index is calculated as follows: The standardized effective ecological supply index is adjusted by introducing a supply and demand balance adjustment coefficient, and the adjusted result is used as the numerator. The ecological fatigue degree is weighted by introducing a historical fatigue degree weighting coefficient, and the sum of the human activity pressure index, the weighted ecological fatigue degree, and the small constant is used as the denominator. The calculated ratio is the ecological carrying capacity index, where an index greater than 1 indicates ecological surplus and less than 1 indicates ecological overload.

[0015] Preferably, the historical fatigue weighting coefficient ranges from 0.3 to 0.6, and is used to adjust the weight of the impact of historical accumulated pressure on the current bearing capacity assessment result.

[0016] This invention provides a remote sensing estimation method for ecological carrying capacity. It has the following beneficial effects: 1. This invention constructs a human activity stress index based on surface temperature correction. Addressing the radiation saturation problem in nighttime light data in areas of high human activity, it utilizes surface temperature as a nonlinear correction factor to adjust the gain of the light data. This method leverages the response characteristics of thermal infrared data to high-energy-consuming areas of the surface, constructing a gain coefficient through an sigmoid function. This quantifies the intensity of human activity exceeding the sensor's radiance saturation threshold, thereby improving the model's numerical discrimination of stress distribution in urban core areas and industrial heat source regions.

[0017] 2. This invention integrates the neighborhood thermal stress mechanism and the supply and demand dimension alignment strategy. On the one hand, by calculating the neighborhood thermal potential difference based on distance weight, it quantifies the inhibitory effect of the surrounding high temperature environment on the external thermal radiation of the central pixel, making up for the shortcomings of traditional methods that only consider the internal environmental attributes of the pixel. On the other hand, it introduces the regional theoretical maximum value to normalize the potential net primary productivity after environmental reduction, and transforms biophysical quantities into dimensionless exponents, solving the problem that the supply-side physical units and the demand-side relative exponents cannot be directly algebraically differencing due to their different dimensions.

[0018] 3. This invention establishes a recursive model for ecological fatigue with a data missing compensation mechanism. Unlike traditional single-phase static assessment methods, this model introduces historical cumulative terms through a time-series recursive algorithm, enabling the calculation of cumulative damage and recovery levels of the ecosystem under long-term supply-demand imbalance. Furthermore, to address the issue of time-series data being susceptible to cloud interference and missing data, a numerical compensation mechanism based on natural attenuation laws is established. This ensures the continuity of assessment under discontinuous observation conditions and avoids calculation errors caused by abnormally zeroing time-series pressure values ​​due to data missing data. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the main flow of the estimation method of the present invention; Figure 2 This is a schematic diagram illustrating the process of constructing a multi-source data spatiotemporal reference according to the present invention. Figure 3 This is a schematic diagram of the process for constructing the human activity stress index based on surface temperature correction according to the present invention. Figure 4 This is a schematic diagram illustrating the specific processing logic of the nonlinear thermal environment correction model of the present invention; Figure 5 This is a schematic diagram of the process for calculating the spatially coupled radiation stress factor based on the neighborhood thermal potential difference in this invention. Figure 6 This is a schematic diagram of the process for calculating the effective ecological supply after multidimensional environmental constraints and dimensional unification according to the present invention. Figure 7 This is a schematic diagram illustrating the process of recursively updating ecological fatigue with a missing compensation mechanism according to the present invention. Figure 8 This is a schematic diagram illustrating the process of calculating the ecological carrying capacity state index that takes into account the historical memory effect, as presented in this invention. Detailed Implementation

[0020] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] See attached document Figure 1 , Figure 1 This is a schematic flowchart of a remote sensing estimation method for ecological carrying capacity according to an embodiment of the present invention. This embodiment proposes a remote sensing estimation method for ecological carrying capacity, which includes the following steps: Step S1 involves establishing a unified, standardized geographic raster space for the multi-source heterogeneous data involved in the ecological assessment, including optical remote sensing imagery, thermal infrared imagery, nighttime light data, and elevation data. Optical remote sensing imagery forms the basis for subsequent ecological supply-side calculations, specifically used to invert the Normalized Difference Vegetation Index (NDVI). This index will be used as the horizontal axis variable in step S4 to construct the temperature-vegetation feature space to calculate water deficit, and also as a core input parameter for the light energy utilization model to calculate potential net primary productivity. Thermal infrared imagery is used to invert land surface temperature, a core environmental variable of this invention, which will be used throughout subsequent steps: as a nonlinear gain factor to correct nighttime light data in step S2, as basic data for calculating neighborhood thermal potential difference in step S3, and as the vertical axis variable in constructing the feature space in step S4. Elevation data serves as a terrain constraint factor for subsequent calculations of potential net primary productivity and elevation correction of land surface temperature. Depending on the different physical attributes of the data, a strategy combining bicubic convolution interpolation and nearest-neighbor interpolation is employed to map source data of different resolutions onto this standard raster. Meanwhile, differentiated temporal synthesis processing is performed. For vegetation indices, maximum values ​​are used to synthesize the data to reflect the optimal growth state. For surface temperature data, effective value averaging based on quality control is used to synthesize the data to eliminate instantaneous abnormal fluctuations, thereby generating a spatiotemporally consistent and physically robust basic dataset.

[0022] Step S2 involves calling the standardized land surface temperature data generated from the thermal infrared imagery in step S1, along with the standardized nighttime light data. To address the saturation overflow problem of traditional nighttime light data in areas of high human activity, land surface temperature is introduced as a correction factor. A nonlinear mapping function is used to convert the standardized land surface temperature data into adjustment coefficients, which are then used to weight and correct the standardized nighttime light data. This process leverages the physical characteristics of high temperature accompanied by high energy consumption to distinguish between high-intensity industrial or commercial heat sources and ordinary cold light sources, thereby quantifying the relative human activity pressure index that allows for exceeding saturation limits.

[0023] Step S3, based on the standardized land surface temperature data generated from the thermal infrared imagery in step S1, achieves strong spatial coupling. By introducing the principle of thermodynamic gradient, it calculates the weighted average thermal potential difference within the neighborhood window centered on the target pixel. This calculation process eliminates the influence of neighborhood window scale variations on the numerical magnitude through distance attenuation weighting matrix and averaging, quantifies the thermal radiation intensity exerted on the central pixel by the high-temperature environment in the neighborhood, and transforms it into a normalized thermal radiation suppression coefficient. This coefficient characterizes the lateral suppression effect of the surrounding thermal environment on the ecological function of the central pixel and will directly participate in the subsequent reduction calculation of effective supply-side quantities.

[0024] Step S4 integrates the vegetation index retrieved from optical remote sensing images, the land surface temperature retrieved from thermal infrared images, and the potential net primary productivity calculated using elevation data from Step S1. Based on the obtained potential net primary productivity, a dual environmental constraint reduction and dimensional normalization process is performed. The temperature-vegetation drought index is used to characterize the degree of water deficit, and the thermal radiation suppression coefficient output from Step S3 is used to characterize the degree of thermal environmental stress, thus correcting the potential productivity. More importantly, to achieve alignment of physical dimensions on both the supply and demand sides, the regional theoretical maximum value is introduced to standardize the corrected productivity, generating a dimensionless standardized effective ecological supply index. This index will serve as the core input for the supply side, laying the mathematical foundation for subsequent algebraic difference calculations.

[0025] Step S5 involves inputting the relative human activity pressure index calculated in step S2 and the standardized effective ecological supply index calculated in step S4. A time-series recursive model is then constructed. Within each time step, different update strategies are selected based on data validity: when data is valid, fatigue is updated based on the difference between the current human activity pressure index and the standardized effective ecological supply index; if a deficit exists, pressure is accumulated; if a surplus exists, recovery is performed. When data is missing, historical fatigue is compensated and continued based on natural decay patterns within a tolerable threshold. This ensures that the historical state of the ecosystem can be remembered and transmitted to the current moment during long-term time-series monitoring.

[0026] Step S6: Using the standardized effective ecological supply index output in step S4 as the numerator, and the sum of the human activity pressure index calculated in step S2 and the current ecological fatigue level output in step S5 as the denominator, the final carrying capacity state index is calculated. The carrying capacity state index not only reflects the current supply-demand balance but also, by introducing a fatigue term, quantifies the reduction effect of historical long-term overload on the current ecosystem's carrying capacity through a mathematical model, thereby achieving a dynamic and refined assessment of the regional ecological security pattern.

[0027] See attached document Figure 2 , Figure 2This is a schematic diagram of the multi-source data spatiotemporal benchmark construction process according to an embodiment of the present invention. Before constructing the ecological carrying capacity estimation model, a strict and unified spatiotemporal data benchmark must be established. Step S1 specifically performs spatial standardization resampling and temporal cleaning and synthesis of multi-source data.

[0028] Step S101: Define the standard projected coordinate system and unified spatial resolution for the study area, mapping all input data to this standard grid. The specific projected coordinate system can be WGS84 UTM projection or other equal-area projections, with the spatial resolution set to a fixed value of 500 to 1000 meters based on the scale of the study area. During this process, differentiated resampling strategies are implemented for data with different physical attributes. For surface temperature (derived from thermal infrared imagery), vegetation index (derived from optical imagery), and elevation data (derived from digital elevation models), these are defined as physical field data with continuous gradient characteristics in space, and resampling is performed using a bicubic convolution interpolation algorithm. This algorithm uses the grayscale values ​​of 16 pixels surrounding the sampling point to perform cubic polynomial fitting, smoothing the surface gradient while maintaining data continuity and avoiding blocky artifacts. For nighttime light data, these are defined as data with discrete radiation characteristics, and resampling is performed using a nearest-neighbor interpolation algorithm. The algorithm directly selects the original pixel value closest to the sampling point as the new value, preserving the original image's radiance value and spatial texture boundary, preventing the value from spreading to non-luminous areas, thereby maintaining the sharpness of the light patch.

[0029] Step S102 employs different time-series synthesis strategies for different physical parameters to avoid introducing systematic biases. For vegetation index data, the maximum value synthesis method is used. This method selects the maximum pixel value within the monthly time window as the output, utilizing the characteristic that the vegetation index value decreases under cloud and fog interference. By taking the maximum value, the attenuation effect of clouds, fog, and aerosols on the optical image spectrum is removed, reflecting the state of most vigorous vegetation growth. For surface temperature data, if the traditional maximum value synthesis method is used, it will retain instantaneous extreme abnormal high temperatures or sensor noise, leading to an overestimation of regional thermal environmental pressure. Therefore, this step uses an effective value averaging synthesis method based on quality control weights for surface temperature. Specifically, the quality control file of the remote sensing data is used to identify and remove invalid pixels covered by clouds and shadows, and the remaining effective pixels within the window period are averaged arithmetically or by weighted average. This processing method eliminates cloud interference while smoothing out instantaneous thermal anomaly fluctuations, obtaining more stable surface temperature data that represents the monthly average thermal environment level. The specific computational processes of the bicubic convolution interpolation algorithm, the nearest neighbor interpolation algorithm, and the maximum value synthesis method mentioned above are well-known techniques to those skilled in the art and will not be elaborated upon here.

[0030] See attached document Figure 3 , Figure 3This is a schematic diagram illustrating the process of constructing a human activity stress index based on surface temperature correction according to an embodiment of the present invention. This process aims to address the numerical saturation problem caused by the limitation of sensor radiation detection thresholds in traditional nighttime light data in urban centers or densely industrial areas. By incorporating surface thermal environment characteristics, it reconstructs a stress index that can characterize the intensity of high-intensity human activities.

[0031] Step S201: Perform dimensionless processing of multi-source data. The standardized nighttime light data and standardized land surface temperature data generated in step S1 are called. Since there are significant differences in their physical units and numerical magnitudes, direct fusion would cause calculation errors. A range normalization algorithm is used to linearly stretch the nighttime light data and land surface temperature data respectively, mapping them to a closed interval between 0 and 1 to generate standardized nighttime light data. and standardized surface temperature data The specific mathematical operations of the range standardization algorithm are well-known to those skilled in the art and will not be elaborated upon here.

[0032] See attached document Figure 4 , Figure 4 This is a schematic diagram illustrating the specific processing logic of the nonlinear thermal environment correction model in step S202 according to an embodiment of the present invention.

[0033] Step S202: Construct a nonlinear thermal environment gain adjustment model. Surface temperature data is used as a nonlinear correction factor to weight and correct the standardized nighttime light data. This step is based on the physical correlation between the surface thermal environment and high-intensity human activities. High-energy-consuming industrial production or high-density commercial activities are usually accompanied by significant heat emissions, leading to increased local surface temperatures, while ordinary street lighting or low-density residential areas do not exhibit this thermal characteristic. By introducing an sigmoid function to construct a nonlinear mapping relationship, the standardized surface temperature is transformed into a gain coefficient for nighttime lights, thereby distinguishing luminous areas of different properties.

[0034] The specific formula for calculating the human activity stress index at the target pixel location is as follows: ; In the formula, This represents the human activity stress index at the calculated pixel location. It should be noted that this index is a relative intensity indicator. Since the correction term in parentheses is greater than one, the final calculated stress index value is allowed to exceed 1.0 to characterize high-intensity overload human activity stress. These represent standardized nighttime light and surface temperature data, respectively. This represents the thermal environment regulation weighting coefficient, with a value ranging from 0.3 to 0.5; The slope factor represents the sigmoid function, ranging from 10 to 20. This parameter controls the model's sensitivity to temperature changes. The larger the value, the steeper the rise of the function curve near the inflection point, indicating that the model is more sensitive to surface warming caused by human activities and can more clearly distinguish between high-temperature, high-energy-consumption areas and low-temperature, low-energy-consumption areas. This represents the center offset of the S-shaped function, and its value is usually 0.5. As a threshold for the inflection point of the temperature response, it is ensured that the output value of the S-shaped function increases significantly only when the surface temperature of the target pixel exceeds the regional average, thus generating a positive pressure gain; conversely, for low-temperature areas, the correction term approaches 0, maintaining the numerical characteristics of the original light data. Through the above nonlinear correction, a refined quantification of pressure in areas of high-intensity human activity is achieved.

[0035] See attached document Figure 5 , Figure 5 This is a schematic diagram illustrating the process of constructing a radiation stress factor based on neighborhood thermal potential difference according to an embodiment of the present invention. This process aims to address the problem that traditional ecological assessments often focus only on the vertical environmental attributes of a pixel itself, neglecting the lateral radiation stress exerted by the neighborhood environment on the central pixel. By introducing the principle of thermodynamic gradient, a thermal radiation suppression coefficient capable of characterizing spatial coupling effects is constructed.

[0036] Step S301: Define a square sliding window as the spatial neighborhood, centered on the target pixel. The side length of the sliding window is specifically set as follows: Pixel to The pixels correspond to a physical spatial scale of 1.5 km to 5 km to cover the effective urban heat island radiation radius and ensure that the thermal impact of the surrounding environment on the center point can be captured.

[0037] Step S302: Calculate the normalized average thermal potential difference based on distance attenuation weights. To avoid the drastic impact of changes in the neighborhood window size on the magnitude of the cumulative thermal potential difference, this step no longer uses a simple summation method, but instead employs a weighted average strategy to calculate the thermal potential difference. This calculation process first determines the spatial distance of each pixel within the neighborhood relative to the central pixel, constructs a distance weight matrix, and then combines standardized land surface temperature data to quantify the heat inflow intensity from surrounding pixels to the central pixel. The specific formula for calculating the normalized average thermal potential difference is as follows: ; In the formula, This represents the normalized average thermal potential difference, and this value is scale-invariant. It is the reciprocal of the distance weight, i.e. ,in For neighboring pixels To the center pixel ( The Euclidean distance is such that the closer the distance, the greater the weight. The calculation ensures that only the positive temperature difference where the surrounding temperature is higher than the center temperature is calculated, that is, only the stress of heat inflow on the ecology is considered, and the influence of cold source is ignored; the denominator is the sum of all weights within the window, which is used to average the thermal potential difference. To prevent the use of tiny constants with a denominator of zero, the value of this constant is taken as follows in this embodiment. .

[0038] Step S303 maps the average thermal potential difference to a normalized thermal radiation suppression coefficient. Based on biophysical principles, the impact of environmental stress on ecological functions typically exhibits a non-linear decay characteristic. Therefore, this step uses an exponential decay function to convert the physical temperature difference into a dimensionless suppression coefficient between 0 and 1. The calculation formula is as follows: ; In the formula, Indicates the thermal radiation suppression coefficient; This is the thermal sensitivity decay constant. Since the influence of the window size has been eliminated by the average thermal potential difference of the input, this constant can remain relatively stable, and its value ranges from 2.0 to 5.0. The larger the value, the more sensitive the ecosystem is to external thermal radiation; that is, even a small temperature difference can produce a large inhibition coefficient. This coefficient will be used to reduce productivity in the subsequent step S4.

[0039] See attached document Figure 6 , Figure 6 This is a schematic diagram illustrating the calculation process of effective ecological supply under multidimensional environmental constraints according to an embodiment of the present invention. The process aims to transform the potential productivity of an ecosystem into a standardized effective supply index comparable in dimensionality to the human activity stress index, while simultaneously taking into account the limiting effects of water and thermal environments on ecological functions.

[0040] Step S401: Perform water deficit feature extraction. Based on the normalized vegetation index obtained from optical remote sensing image inversion in step S1 and the land surface temperature obtained from thermal infrared image inversion, a temperature-vegetation feature space corresponding to each pixel is constructed. To ensure that the feature space can accurately reflect the hydrothermal limit state of the region, the selection range of pixel samples is set to cover a sufficiently large spatial scale (e.g., watershed scale or provincial scale) to ensure that the sample set includes two types of endmember features: extremely arid bare soil and water-sufficient vegetation. Within this two-dimensional feature space, the boundary of the maximum land surface temperature under different vegetation cover is defined as the dry edge (representing the water stress limit), and the boundary of the minimum land surface temperature is defined as the wet edge (representing sufficient water supply). Linear regression is used to fit the dry edge equation and the wet edge equation respectively, and then the temperature-vegetation drought index of each pixel is calculated. The calculation results are truncated from 0 to 1, where a value close to 1 represents an extreme water deficit state. The specific algorithms for feature space construction and dry-wet edge linear fitting are well known to those skilled in the art and will not be described in detail here.

[0041] Step S402: Perform standardized calculation of the ecological supply index. This involves comprehensively utilizing potential net primary productivity (…). ), water deficit status and thermal radiation stress factors ( The final effective supply calculation is performed. This includes the potential net primary productivity (…). It is calculated based on a light energy utilization model (such as the CASA model), and its calculation input strictly depends on the optical remote sensing imagery NDVI data and meteorological radiation data provided in step S1. Given that NPP has specific physical units (usually...), Furthermore, the numerical magnitude (e.g., 0 to 1500) differs significantly from the dimensionless human activity pressure index calculated in step S2 (typically 0 to 2.0). Direct algebraic calculations would cause the model to fail. Therefore, this step introduces a theoretical maximum productivity reference value, normalizing the physical quantity into a dimensionless relative supply index. Simultaneously, a dual environmental constraint is applied to reduce potential productivity: the TVDI characterizes moisture limitation, and the thermal radiation suppression coefficient output in step S3 characterizes thermal environmental limitation.

[0042] The specific formula for calculating standardized effective ecological supply is as follows: ; In the formula, The standardized effective ecological supply index is mapped to a range of 0 to 1, making it comparable to the human activity pressure index. Indicates potential net primary productivity; Indicates temperature and vegetation drought index; This represents the thermal radiation suppression coefficient calculated in step S3. This is the thermal stress correction term; This represents the historical maximum potential productivity reference value or theoretical maximum value within the study area (e.g., a value of 1500). ), used to achieve the normalization of physical dimensions.

[0043] See attached document Figure 7 , Figure 7 This is a schematic diagram illustrating a process for recursively updating ecological fatigue with a missing data compensation mechanism according to an embodiment of the present invention. This process primarily addresses the temporal correlation problem in ecological assessment, namely, that the current ecological carrying capacity depends not only on the current supply and demand balance but also on the continuous influence of historical accumulated pressure. By constructing a recursive model with memory capabilities, continuous tracking of the ecosystem's fatigue state is achieved, and a compensation mechanism is designed to address the common spatiotemporal missing data phenomenon in remote sensing data.

[0044] Step S501: Perform time series initialization of the model. At the start time of the time series (set to...) Assuming the ecosystem of the study area is in initial equilibrium, the ecological fatigue matrix will be used. Initialize it as an all-zero matrix. This matrix will serve as the baseline for subsequent recursive calculations, used to accumulate the ecological deficit generated at subsequent time steps.

[0045] Step S502: Perform differentiated fatigue recursive update based on data validity status. This step first reads the data validity identifier at the current moment. This identifier is generated based on the quality control file of the remote sensing image (1 indicates valid data, 0 indicates missing data). The model selects to enter either the accumulation-recovery mode or the memory-decay mode based on this identifier.

[0046] when When the index equals 1, the model enters the accumulation-recovery mode. The system retrieves the human activity stress index output from step S2 and the standardized effective ecological supply index output from step S4 for differential calculation. During this process, the ecosystem's self-repair mechanism and new stress inputs are introduced. On the one hand, the historically accumulated fatigue will decrease proportionally with the ecosystem's self-regulation; on the other hand, if the current stress index exceeds the adjusted supply index, the resulting net deficit will be added to the fatigue level.

[0047] when When the value is 0, the model enters memory-decay mode. For data loss due to cloud cover or sensor malfunction, instead of directly zeroing out fatigue, the previous state is maintained according to the natural decay law. To prevent fatigue from being incorrectly decayed to zero due to long-term data loss (i.e., avoiding the logical deviation that stress automatically disappears as long as it is not observed), this step sets a maximum tolerable data loss threshold (set to 6 consecutive time steps of data loss, approximately six months). If the data loss duration is within the threshold, historical fatigue is decayed exponentially; if it exceeds the threshold, decay stops and the previous value is maintained until new valid observation data is obtained.

[0048] The recursive formula for calculating ecological fatigue at a specific moment is as follows: ; In the formula, This represents the ecological fatigue level calculated at the current moment, and this indicator quantifies the historical cumulative load that the ecosystem bears; This indicates the ecological fatigue state at the previous moment, reflecting the model's memory property; The human activity stress index is the output of step S2; The standardized effective ecological supply index is the output of step S4. This serves as a data validity indicator for the current moment (1 indicates valid, 0 indicates missing). This is a supply and demand balance adjustment coefficient used to fine-tune the supply and demand balance benchmark point in different regions. Its value range is usually from 0.8 to 1.2. This coefficient is used to fine-tune the ecological background of different regions in order to determine the relative benchmark point of supply and demand balance. The ecological restoration rate coefficient ranges from 0.1 to 0.2. This coefficient characterizes the self-repair and metabolic capacity of an ecosystem under conditions of no new pressure or surplus. The larger the value, the faster the system recovers from a fatigued state to a healthy state. The natural decay constant, ranging from 0.05 to 0.1, is used to simulate the natural decay of the perception of historical accumulated pressure over time during periods of data loss, ensuring that the model can provide a conservative estimate based on historical trends when observational data is lacking.

[0049] See attached document Figure 8 , Figure 8This is a schematic flowchart illustrating the process of calculating an ecological carrying capacity state index that takes into account the historical memory effect, according to an embodiment of the present invention. This process aims to integrate the independent indicators calculated in previous steps to generate a comprehensive index that can quantitatively reflect the regional ecological security pattern. Unlike traditional static assessment methods, this embodiment introduces a historical burden dimension into the denominator of the carrying capacity equation, thereby achieving dynamic coupling between current supply capacity, current human pressure, and historically accumulated ecological debt.

[0050] Step S601: Assemble and align the model input parameters. The system retrieves the standardized effective ecological supply index output from step S4. As a numerator characterizing the positive support capacity of an ecosystem, the human activity stress index output from step S2 is retrieved. As the first denominator term representing the intensity of negative interference at the current moment, the ecological fatigue level at the current moment is retrieved from the output of step S5. This serves as the second denominator term, representing the historical cumulative load. During this process, it is ensured that the spatial resolution of all input data remains strictly consistent with the projected coordinate system, and point-to-point algebraic operations are performed at the pixel scale.

[0051] Step S602 involves constructing a carrying capacity state equation that takes into account the historical memory effect. The core logic of this step is to define ecological carrying capacity as the ratio of ecological supply to total pressure, where total pressure is reconstructed as a weighted sum of immediate pressure and historically lagged pressure. A mathematical model quantifies the reducing effect of long-term historical overload on the current ecosystem's carrying capacity; that is, the system is considered safe only when ecological supply can not only cover current consumption but also repay historical debts.

[0052] The specific formula for calculating the ecological carrying capacity state index is as follows: ; In the formula, Indicates the ecological carrying capacity status index; The effective ecological supply index is standardized, with a value range between 0 and 1, reflecting the actual ecological output capacity after being constrained by the hydrothermal environment. The Human Activity Stress Index reflects direct human interference within the current time step. For ecological fatigue, the system's cumulative deficit status in historical time series is stored; This is a supply-demand balance adjustment coefficient, with a value range of 0.8 to 1.2. This coefficient is used to fine-tune the baseline carrying capacity of different ecological functional zones. This is the historical fatigue weighting coefficient, ranging from 0.3 to 0.6. This parameter is used to adjust the weight of the influence of historical cumulative pressure on the current bearing capacity assessment results. The value setting directly determines the model's ability to trace back historical debts: the higher the value, the worse the ecosystem's resilience to historical damage, and the stronger the constraint of historical fatigue on the current carrying capacity, meaning that more current surplus is needed to offset historical deficits. It is a small constant, and its value is usually 1. This is used to prevent calculation overflow errors caused by the denominator (the sum of immediate pressure and historical pressure) being zero. When, it represents ecological surplus; when At that time, ecological overload is characterized. Through the above steps, this invention successfully solves the problem of dimensional alignment of supply and demand data, and achieves precise coupling of spatiotemporal dimensions using relative indices.

[0053] Step S603: Perform threshold determination of bearing capacity status. Based on the calculated... Numerical values ​​are used to classify and assess the regional ecological security pattern. When When this occurs, it is determined to be an ecological surplus state, indicating that the current ecological supply capacity is sufficient to support human activities and gradually repair historical damage; when... When the current ecological supply is insufficient to balance the overall pressure, the system is deemed to be in a state of ecological overload, indicating that the current ecological supply is insufficient to balance the overall pressure. The system will face the risk of degradation and will further accumulate ecological fatigue in the next time step. Through the above calculation logic, this invention explicitly incorporates the cumulative effect of time into the spatial carrying capacity assessment system. Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A remote sensing estimation method for ecological carrying capacity, characterized in that, The method includes the following steps: S1. Establish a standard geographic raster space, resample and synthesize multi-source data to generate a spatiotemporally consistent basic dataset of vegetation index, surface temperature and nighttime light. S2 uses surface temperature as a nonlinear correction factor to adjust the gain of nighttime light data and calculates the human activity stress index. S3 calculates the weighted average thermal potential difference within the neighborhood window based on the thermodynamic gradient principle and converts it into a thermal radiation suppression coefficient. S4. The potential net primary productivity is corrected by combining water deficit and the heat radiation inhibition coefficient, and the regional theoretical maximum value is introduced for normalization to obtain the standardized effective ecological supply index. S5. Construct a time-series recursive model. Within each time step, update the current ecological fatigue level based on the historical ecological fatigue level and the current supply-demand difference, according to the data validity. S6. Calculate the ecological carrying capacity status index by combining the standardized effective ecological supply index, human activity pressure index, and ecological fatigue index.

2. The remote sensing estimation method for ecological carrying capacity according to claim 1, characterized in that, In S1, the resampling and timing synthesis specifically include: A standard geographic raster space is defined. For surface temperature and vegetation index data, a bicubic convolution interpolation algorithm is used for resampling to maintain the smoothness of the surface gradient. For nighttime light data, a nearest neighbor interpolation algorithm is used for resampling to preserve the texture boundaries of the original radiance values. For vegetation index data, the maximum value synthesis method is used to generate monthly data; for surface temperature data, the effective value average synthesis method based on quality control weights is used. Specifically, after parsing the quality control file to remove cloud and shadow pixels, the effective pixels within the window period are averaged to eliminate instantaneous thermal anomaly fluctuations.

3. The remote sensing estimation method for ecological carrying capacity according to claim 1, characterized in that, In S2, the human activity stress index is calculated as follows: Range standardization was performed on nighttime light data and surface temperature data respectively. A temperature gain coefficient is constructed using an S-shaped nonlinear function, and the standardized nighttime light data is multiplied by the temperature gain coefficient. The temperature gain coefficient is composed of a base value of 1 and a temperature correction term, and the temperature correction term increases in an S-shaped curve as the standardized surface temperature increases. The human activity pressure index controls the upper limit of the correction range by setting a thermal environment regulation weight coefficient, and ensures that a positive gain is generated only when the temperature exceeds the regional average level by setting a center offset.

4. The remote sensing estimation method for ecological carrying capacity according to claim 3, characterized in that, The parameters in the S-type nonlinear function are set as follows: The thermal environment adjustment weighting coefficient ranges from 0.3 to 0.5 and is used to control the correction magnitude of the surface temperature on the final pressure index. The slope factor of the S-shaped function ranges from 10 to 20 and is used to control the model's sensitivity to temperature changes. The center offset is set to 0.5 as the inflection point threshold for the temperature response.

5. The remote sensing estimation method for ecological carrying capacity according to claim 1, characterized in that, In S3, the calculation of the weighted average thermal potential difference specifically includes: Define a square sliding window centered on the target pixel, and calculate the positive temperature difference between each neighboring pixel and the central pixel within the window; The reciprocal of the distance weight is introduced as a weighting factor to perform a weighted summation of the positive temperature difference. The result is then divided by the sum of all weights within the window and the sum of the infinitesimal constants to obtain the average thermal potential difference with scale invariance. The thermal radiation suppression coefficient is obtained by performing a negative exponential decay transformation on the average thermal potential difference, which includes a thermal sensitivity decay constant, the value of which ranges from 2.0 to 5.

0.

6. The remote sensing estimation method for ecological carrying capacity according to claim 1, characterized in that, In S4, the calculation of the standardized effective ecological supply index specifically includes: A feature space is constructed based on surface temperature and vegetation index. Dry edge equation and wet edge equation are fitted to calculate the temperature-vegetation drought index of the pixel to characterize water deficit. The potential net primary productivity is multiplied by the water deficit correction term and the thermal stress correction term to obtain the corrected productivity; wherein the water deficit correction term is 1 minus the temperature vegetation drought index, and the thermal stress correction term is 1 minus the thermal radiation inhibition coefficient. Dividing the corrected productivity by the historical maximum potential productivity reference value within the study area yields a standardized effective ecological supply index with a value range mapped to 0 to 1.

7. The remote sensing estimation method for ecological carrying capacity according to claim 1, characterized in that, In S5, the ecological fatigue degree is defined as the cumulative amount of historical supply and demand imbalances in the ecosystem, and its recursive update process specifically includes: Determine the validity of the data at the current moment; If the data is valid, calculate the difference between the human activity pressure index and the standardized effective ecological supply index; if the difference is positive, add the difference to the ecological fatigue level of the previous moment after the recovery coefficient decay. If the data is invalid, determine whether the duration of data loss is within the maximum tolerance threshold. If it is within the threshold, apply exponential decay based on the natural decay constant to the ecological fatigue level of the previous time step. If it exceeds the threshold, stop decaying and maintain the state of the previous time step.

8. The remote sensing estimation method for ecological carrying capacity according to claim 7, characterized in that, The parameters involved in the recursive update process are set as follows: When calculating the supply-demand difference, a supply-demand balance adjustment coefficient is introduced to weight the standardized effective ecological supply index. The value of this coefficient ranges from 0.8 to 1.

2. The recovery coefficient ranges from 0.1 to 0.2 and is used to characterize the self-repairing ability of an ecosystem under surplus conditions. The natural decay constant ranges from 0.05 to 0.1 and is used to characterize the natural decay process of pressure during data loss.

9. The remote sensing estimation method for ecological carrying capacity according to claim 1, characterized in that, In S6, the ecological carrying capacity state index is calculated as follows: The standardized effective ecological supply index is adjusted by introducing a supply and demand balance adjustment coefficient, and the adjusted result is used as the numerator. The ecological fatigue degree is weighted by introducing a historical fatigue degree weighting coefficient, and the sum of the human activity pressure index, the weighted ecological fatigue degree, and the small constant is used as the denominator. The calculated ratio is the ecological carrying capacity index, where an index greater than 1 indicates ecological surplus and less than 1 indicates ecological overload.

10. The remote sensing estimation method for ecological carrying capacity according to claim 9, characterized in that, The historical fatigue weighting coefficient ranges from 0.3 to 0.6 and is used to adjust the weight of the impact of historical accumulated pressure on the current bearing capacity assessment results.

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