An improved casa model-based urban tree net primary productivity estimation method

Abstract

The application discloses a kind of urban tree net primary productivity estimation methods based on improved CASA model, belong to ecological remote sensing technical field.The traditional CASA model is low in estimation accuracy in view of the problem that landscape is broken, spatial heterogeneity and environmental stress in urban environment, the application is improved mixed pixel problem by introducing object-oriented fine scale photosynthetically active radiation absorption ratio mapping model, generates the distribution of maximum light energy utilization rate of spatial continuous change, constructs comprehensive environmental stress factor, and is incorporated into urban soil characteristics, landscape fragmentation and tree structure characteristics, combined with photosynthetically active radiation, temperature and water stress factor, and constructs improved urban CASA model.The high-precision, high-resolution estimation of urban tree net primary productivity is realized, and the systematic deviation is effectively reduced, which is used for accurate quantification of urban tree carbon sequestration capacity.

Description

Technical Field

[0001] This invention belongs to the field of ecological remote sensing technology, and in particular relates to a method for estimating the net primary productivity of urban trees based on an improved CASA model. Background Technology

[0002] As a crucial component of urban ecosystems, the accurate quantification of urban forests' carbon sequestration capacity is essential for assessing urban carbon neutrality potential and formulating scientific ecological management strategies. Currently, the CASA model, based on light energy utilization efficiency theory, has become the mainstream model for estimating the net primary productivity of large-scale terrestrial vegetation due to its clear mechanism and readily available parameters. The core of this model lies in simplifying the mass of organic matter fixed by vegetation to the product of its absorbed photosynthetically active radiation and actual light energy utilization efficiency. This framework has been validated for good estimation results in homogeneous natural ecosystems, such as large areas of continuous forests or grasslands.

[0003] However, directly applying this traditional CASA model, designed for natural ecosystems, to urban environments with complex structures and high levels of human interference reveals significant inadequacies, leading to systematic biases in the estimation results and failing to meet the needs of refined management. Specifically, firstly, urban landscapes are highly fragmented, with trees often existing as isolated trees, street trees, or small green patches, interspersed with buildings, roads, and paved surfaces. Traditional models typically rely on low-resolution remote sensing data, such as 500-meter resolution MODIS FPAR products. At this scale, image pixels inevitably contain both vegetation and non-vegetation information, forming "mixed pixels." This results in severe dilution or obfuscation of vegetation spectral signals, making it difficult for the model to effectively capture and represent the fragmented and discrete spatial distribution characteristics of trees within the city, causing distortion in basic parameter extraction and loss of estimation accuracy. Secondly, traditional models typically assume that the same vegetation type has a constant maximum light energy utilization parameter. This assumption is acceptable in natural forests with minimal human intervention, but in urban environments, even trees of the same species exhibit significant differences in growth status and physiological potential due to variations in their specific habitat, level of management, surrounding heat island effect, and shading conditions. Fixed parameters cannot distinguish between thriving trees in the core area of ​​a park and stressed trees growing along roadsides or in building gaps, leading to local-scale estimates that deviate severely from reality.

[0004] More importantly, traditional CASA models primarily consider the limiting effects of temperature and moisture—two universal natural meteorological factors—on light energy utilization, completely ignoring the unique and intense anthropogenic environmental stresses specific to urban ecosystems. In addition to climate influences, urban tree growth is profoundly affected by multiple pressures, including soil compaction, root zone constraints, pollution, and the marginal effects of landscape fragmentation. The lack of quantification and integration mechanisms for these city-specific stresses in existing models is the fundamental reason for the widespread overestimation of urban tree carbon sequestration capacity. Therefore, developing an improved model that can overcome urban spatial heterogeneity, characterize tree physiological differences, and comprehensively quantify city-specific environmental stresses has become a pressing technical challenge for accurately estimating the net primary productivity of urban trees. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention proposes a method for estimating the net primary productivity of urban trees based on an improved CASA model, thereby resolving the issues present in the existing technologies.

[0006] Firstly, to achieve the above objectives, this invention provides a method for estimating the net primary productivity of urban trees based on an improved CASA model, comprising the following steps:

[0007] Acquire multi-source geospatial data of the target urban area, wherein the multi-source geospatial data includes high-resolution remote sensing imagery;

[0008] Based on the high-resolution remote sensing image, generate the fine-scale photosynthetically active radiation absorption ratio for the target patch.

[0009] Construct and calculate the actual light energy utilization rate of spatially heterogeneous vegetation, wherein the actual light energy utilization rate responds to the maximum light energy utilization rate of vegetation in response to meteorological stress factors and spatial variations;

[0010] A carbon sequestration correction factor was constructed by integrating the structural characteristics of urban trees, soil properties, and landscape pattern indicators.

[0011] Based on the fine-scale photosynthetically active radiation absorption ratio, the actual light energy utilization rate, and the carbon sequestration correction factor, the net primary productivity of trees in the target urban area is estimated using a light energy utilization rate model.

[0012] Optionally, the process of acquiring multi-source geospatial data includes:

[0013] Acquire meteorological data, optical remote sensing data, and urban environmental data;

[0014] The optical remote sensing data includes high-resolution multispectral images and coarse-resolution photosynthetically active radiation absorption ratio products.

[0015] The meteorological data, the optical remote sensing data, and the urban environmental data are preprocessed, including projection transformation, resampling, and cropping.

[0016] Optionally, the process of generating fine-scale photosynthetically active radiation absorption ratios for object-oriented patches includes:

[0017] Multi-scale segmentation is performed on the high-resolution remote sensing image to form spectrally homogeneous object patches;

[0018] Establish a functional relationship between the average reflectance of object patches of different land cover categories and the photosynthetically active radiation absorption ratio;

[0019] By combining the coarse-resolution photosynthetically active radiation absorption ratio (RAEP) product, the fine-scale RAEP at the target patch level is obtained through iterative inversion.

[0020] Optionally, the process of constructing and calculating the actual light energy utilization of spatially heterogeneous vegetation includes:

[0021] The maximum light energy utilization rate of spatially continuous distribution is dynamically calculated based on vegetation index and canopy albedo.

[0022] Calculate the temperature stress factor;

[0023] Calculate water stress factors using the surface water index;

[0024] The actual light energy utilization rate is obtained by multiplying the maximum light energy utilization rate of the spatially continuous distribution, the temperature stress factor, and the moisture stress factor.

[0025] Optionally, the process of constructing the carbon fixation correction factor includes:

[0026] Canopy height, soil bulk density, and Shannon diversity index were selected as input indicators.

[0027] The normalized input index is input into a preset calculation model, and the carbon fixation correction factor is output.

[0028] Optionally, the process of estimating tree net primary productivity using a light energy utilization model includes:

[0029] The absorbed photosynthetically active radiation is calculated based on the total solar radiation and the fine-scale photosynthetically active radiation absorption ratio.

[0030] The net primary productivity of the tree is obtained by multiplying the absorbed photosynthetically active radiation, the actual light energy utilization rate, and the carbon fixation correction factor.

[0031] Secondly, the present invention also provides a system for estimating the net primary productivity of urban trees based on an improved CASA model, for implementing a method for estimating the net primary productivity of urban trees based on an improved CASA model, the system comprising:

[0032] The data acquisition module is used to acquire multi-source geospatial data of the target urban area, wherein the multi-source geospatial data includes high-resolution remote sensing images;

[0033] The parameter calculation module is used to generate the fine-scale photosynthetically active radiation absorption ratio of the target patch based on the high-resolution remote sensing image.

[0034] A light energy utilization rate construction module is used to construct and calculate the actual light energy utilization rate of spatially heterogeneous vegetation, wherein the actual light energy utilization rate responds to the maximum light energy utilization rate of vegetation in response to meteorological stress factors and spatial variations.

[0035] The correction factor construction module is used to construct carbon sequestration correction factors by integrating the structural characteristics of urban trees, soil properties, and landscape pattern indicators.

[0036] The net primary productivity estimation module is used to estimate the net primary productivity of trees in the target urban area based on the fine-scale photosynthetically active radiation absorption ratio, the actual light energy utilization rate, and the carbon sequestration correction factor, using a light energy utilization rate model.

[0037] Thirdly, the present invention also provides a computer terminal device, comprising:

[0038] One or more processors;

[0039] A memory, coupled to the processor, for storing one or more programs;

[0040] When the one or more programs are executed by the one or more processors, the one or more processors implement the steps of the method for estimating the net primary productivity of urban trees based on the improved CASA model in the first aspect described above.

[0041] Fourthly, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method for estimating the net primary productivity of urban trees based on the improved CASA model in the first aspect described above.

[0042] Fifthly, the present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the method for estimating the net primary productivity of urban trees based on the improved CASA model in the first aspect described above.

[0043] Compared with the prior art, the present invention has the following advantages and technical effects:

[0044] This invention provides a method for estimating the net primary productivity of urban trees based on an improved CASA model. By introducing an object-oriented, fine-scale photosynthetically active radiation absorption ratio mapping model, this invention effectively improves the vegetation signal confusion problem caused by mixed pixels in fragmented urban landscapes, achieving accurate identification and representation of scattered urban trees. By constructing a spatially continuously varying maximum light energy utilization distribution, it breaks the traditional fixed parameter assumption and can truly reflect the spatial heterogeneity of tree physiological potential caused by habitat differences and human management. By comprehensively considering soil characteristics, landscape fragmentation, and tree structural features to construct an urban environmental stress correction factor, it incorporates the urban-specific constraints not considered in traditional models into the quantification system, thereby effectively correcting the systematic overestimation bias caused by neglecting these stresses. Finally, this invention integrates the above improvements to form an improved CASA model suitable for complex urban environments, significantly improving the precision and overall accuracy of estimating the net primary productivity of urban trees. Attached Figure Description

[0045] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings:

[0046] Figure 1 This is a flowchart of the method for estimating the net primary productivity of urban trees according to an embodiment of the present invention;

[0047] Figure 2 This is a comparison chart of the estimation results of the improved CASA model and the traditional model in an embodiment of the present invention. Detailed Implementation

[0048] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0049] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0050] Example 1

[0051] like Figure 1 As shown, this embodiment provides a method for estimating the net primary productivity of urban trees based on an improved CASA model, including:

[0052] Acquire multi-source geospatial data of the target urban area, wherein the multi-source geospatial data includes high-resolution remote sensing imagery;

[0053] Based on the high-resolution remote sensing image, generate the fine-scale photosynthetically active radiation absorption ratio for the target patch.

[0054] Construct and calculate the actual light energy utilization rate of spatially heterogeneous vegetation, wherein the actual light energy utilization rate responds to the maximum light energy utilization rate of vegetation in response to meteorological stress factors and spatial variations;

[0055] A carbon sequestration correction factor was constructed by integrating the structural characteristics of urban trees, soil properties, and landscape pattern indicators.

[0056] Based on the fine-scale photosynthetically active radiation absorption ratio, the actual light energy utilization rate, and the carbon sequestration correction factor, the net primary productivity of trees in the target urban area is estimated using a light energy utilization rate model.

[0057] As one implementation method in this embodiment, the process of acquiring multi-source geospatial data includes:

[0058] Acquire meteorological data, optical remote sensing data, and urban environmental data;

[0059] The optical remote sensing data includes high-resolution multispectral images and coarse-resolution photosynthetically active radiation absorption ratio products.

[0060] The meteorological data, the optical remote sensing data, and the urban environmental data are preprocessed, including projection transformation, resampling, and cropping.

[0061] As one implementation method in this embodiment, the process of generating the fine-scale photosynthetically active radiation absorption ratio of object-oriented patches includes:

[0062] Multi-scale segmentation is performed on the high-resolution remote sensing image to form spectrally homogeneous object patches;

[0063] Establish a functional relationship between the average reflectance of object patches of different land cover categories and the photosynthetically active radiation absorption ratio;

[0064] By combining the coarse-resolution photosynthetically active radiation absorption ratio (RAEP) product, the fine-scale RAEP at the target patch level is obtained through iterative inversion.

[0065] As one implementation method in this embodiment, the process of constructing and calculating the actual light energy utilization rate of spatially heterogeneous vegetation includes:

[0066] The maximum light energy utilization rate of spatially continuous distribution is dynamically calculated based on vegetation index and canopy albedo.

[0067] Calculate the temperature stress factor;

[0068] Calculate water stress factors using the surface water index;

[0069] The actual light energy utilization rate is obtained by multiplying the maximum light energy utilization rate of the spatially continuous distribution, the temperature stress factor, and the moisture stress factor.

[0070] As one implementation method in this embodiment, the process of constructing the carbon fixation correction factor includes:

[0071] Canopy height, soil bulk density, and Shannon diversity index were selected as input indicators.

[0072] The normalized input index is input into a preset calculation model, and the carbon fixation correction factor is output.

[0073] As one implementation method in this embodiment, the process of estimating the net primary productivity of trees using a light energy utilization model includes:

[0074] The absorbed photosynthetically active radiation is calculated based on the total solar radiation and the fine-scale photosynthetically active radiation absorption ratio.

[0075] The net primary productivity of the tree is obtained by multiplying the absorbed photosynthetically active radiation, the actual light energy utilization rate, and the carbon fixation correction factor.

[0076] This invention proposes a method for estimating net primary productivity (NPP) of urban trees based on an improved CASA model. Addressing the spatial heterogeneity and complex, unique environmental stresses on tree growth in cities, this method proposes an improved model that comprehensively considers both spatial heterogeneity and urban environmental stresses, improving and integrating the traditional CASA model in three key dimensions: First, an object-oriented fine-scale photosynthetically active radiation absorptive ratio (FPAR) mapping model is introduced to improve the mixed-cell problem in FPAR calculation under fragmented urban landscapes. Simultaneously, it considers the maximum light energy utilization rate (…). The heterogeneity in urban ecosystems generates continuously changing... Distribution. Furthermore, a comprehensive environmental stress factor is constructed, incorporating the limiting effects of urban-specific soil characteristics, landscape fragmentation, and tree structural features on growth into the model. Through the above improvements and innovations, this invention combines photosynthetically active radiation (SOL), temperature, and water stress factors to construct an urban CASA model, supporting high-precision and high-resolution estimation of tree NPP at the urban scale. Specific steps include:

[0077] Step 1: First, acquire multi-source geospatial data of similar temporal phases within the study area, including: meteorological data, including total solar radiation (SOL), monthly average temperature, and precipitation data; optical remote sensing data, including high-resolution multispectral imagery (such as Landsat 8 OLI) and MODIS FPAR products; and urban environmental data (soil properties, vegetation structure properties, landscape fragmentation properties, etc.). Perform preprocessing on all input data, including projection transformation, resampling, and cropping.

[0078] Step 2: Calculate object-oriented fine-scale photosynthetically active radiation absorptive ratio (FPAR). This method calculates object-based fine-scale FPAR within the study area using a Remote Sensing Trend Surface (RSTS) framework. First, high-resolution imagery (e.g., Landsat 8) is segmented at multiple scales, dividing the complex urban surface into spectrally homogeneous object patches. Next, linear function models between high-resolution surface reflectance and FPAR are established for different land cover categories. Then, based on the constraint relationship between coarse-resolution FPAR products and high-resolution FPAR, and combined with a distribution iteration strategy, high-resolution FPAR distribution data reflecting urban surface fragmentation characteristics is finally calculated.

[0079]

[0080] It is the FPAR value of the coarse resolution pixel. It represents the proportion of each object category to the area of ​​a MODIS pixel. It is the total number of categories. It is a constant term in a linear model. These are the coefficients of the k-th band in the linear model. This represents the number of bands in the image used in the calculation. In fine-scale images, the first Average reflectance of objects in each wavelength band.

[0081] Step 3: Calculate the actual light energy utilization rate considering spatial heterogeneity ( This step aims to obtain the actual light energy utilization rate, which is affected not only by meteorological environment but also by spatial location and vegetation physiological potential. This invention constructs the maximum light energy utilization rate in spatially heterogeneous environments (…). The estimation model selects the maximum enhanced vegetation index during the growing season. To characterize the potential photosynthetic capacity of vegetation, the minimum visible light albedo ( ) was selected. The light-trapping capacity of the canopy structure is characterized, and the spatially continuous distribution is calculated. :

[0082]

[0083] In the formula, Determined based on EVI, Determined based on reflectivity in the visible light band.

[0084] Furthermore, considering the limiting effect of meteorological conditions on photosynthesis, including temperature stress and water stress, temperature stress factors include low temperature limiting factors ( ) and optimal temperature deviation factor ( Considering the characteristics of urban surface hydrology, the surface water index (LSWI), which is related to vegetation moisture content, is used to characterize water stress.

[0085]

[0086] In the formula, This is the highest surface water index during the growing season.

[0087] Based on the above spatial physiological parameters and meteorological environmental parameters, the final actual light energy utilization rate is calculated by multiplication.

[0088] Step 4: Construct a carbon sequestration correction factor based on urban tree characteristics. Indicators were selected from three dimensions: tree structure, soil physical properties, and landscape pattern. Crown height (CHM) was selected to characterize tree vertical structure and resource acquisition capacity; bulk density (BD) was selected to characterize the restriction of root systems by urban soil compaction; and Shannon's Diversity Index (SHDI) was selected to characterize landscape fragmentation and edge effects. These characteristic values ​​were extracted from the target patches to construct a comprehensive carbon sequestration correction factor. :

[0089]

[0090] Where logsig represents the logistic sigmoid function, and BD and SHDI are the normalized values.

[0091] Step 5: Estimate the net primary productivity (NPP) of urban trees. Based on the parameters calculated in the above steps, the absorbed photosynthetically active radiation (IPAR) and actual light energy utilization rate (NPP) will be included. ) and urban carbon sequestration correction factor ( Multiplying these components generates an urban CASA model. This improved CASA model formula is then used to calculate the net primary productivity of urban trees, ultimately yielding a high-precision estimate of carbon sequestration capacity. The model is as follows:

[0092]

[0093] In the formula, NPP represents the net primary productivity of urban trees (unit: SOL represents total solar radiation; 0.5 is the proportion of photosynthetically active radiation available to vegetation to total solar radiation. By introducing... The model effectively corrects the systematic overestimation error of traditional methods in urban areas.

[0094] Based on this, the present invention provides a method for estimating the net primary productivity of urban trees based on an improved CASA model, the beneficial effects of which include the following:

[0095] This invention enhances the model's fine-scale expressiveness, accurately depicting the spatially heterogeneous distribution characteristics of urban trees. This advantage is achieved through improvements in steps 2 and 3. Firstly, fine-scale parameter calculation is implemented. Existing technologies often rely on coarse-resolution data (such as 500 m MODIS FPAR products), which suffers from severe mixed-pixel effects in urban environments. This invention introduces object-based fine-scale photosynthetically active radiation absorptive ratio (FPAR), refining the computational unit from coarse pixels to specific tree patch objects. This refined input allows the model to accurately identify and calculate the carbon sequestration contribution of scattered street trees, riverside walkways, and other small green spaces, solving the problem of information loss in highly fragmented urban areas in traditional models. Simultaneously, it enables parameter calculation for spatial heterogeneity. Traditional CASA models assume the maximum light energy utilization rate (FPAR) of the same vegetation type. The carbon sequestration capacity of urban trees varies greatly due to human management and habitat differences. This invention utilizes the Enhanced Vegetation Index (EVI) and visible light albedo to construct a dynamic calculation model, generating spatially continuously varying... This allows the model to realistically reproduce the differences in the physiological efficiency of trees at different spatial locations.

[0096] By considering the stresses of complex urban environments on tree growth, systematic biases in the model estimation process are effectively eliminated. This advantage stems from step 4. Current research models can reflect the limitations of conventional meteorological factors on vegetation photosynthesis, but they are insufficient in reflecting the environmental pressures specific to cities, leading to significant systematic overestimation in urban areas. This invention innovatively constructs a correction factor for urban carbon sequestration capacity. Soil bulk density (BD) (reflecting the restriction of root system by soil compaction), Shannon diversity index (SHDI) (reflecting the edge effect of landscape fragmentation), and canopy height (CHM) (reflecting the differences in tree vertical structure) were incorporated into the calculation system. By quantifying the negative stress effects of these non-meteorological factors in multiple dimensions, the model successfully corrected the theoretical calculation values, significantly reducing the estimation error. The RMSE decreased from 608.87 to 37.32 kgC / year (e.g., ...). Figure 2As shown in the figure, this enables an accurate quantification of the true carbon sequestration capacity of urban trees.

[0097] Example 2

[0098] In this embodiment, a computer terminal device is provided, including:

[0099] One or more processors;

[0100] A memory, coupled to the processor, for storing one or more programs;

[0101] When the one or more programs are executed by the one or more processors, the one or more processors implement the steps of the above-described method for estimating the net primary productivity of urban trees based on the improved CASA model.

[0102] In this embodiment, a computer-readable storage medium is also provided, on which a computer program is stored. When the computer program is executed by a processor, it implements the steps of the above-described method for estimating the net primary productivity of urban trees based on the improved CASA model.

[0103] In this embodiment, an electronic device is also provided, including a memory and a processor. The memory stores a computer program, and the processor is configured to run the computer program to perform the steps of the above-described method for estimating the net primary productivity of urban trees based on the improved CASA model.

[0104] In this embodiment, a computer program product is also provided, including a computer program that, when executed by a processor, implements the steps of the above-described method for estimating the net primary productivity of urban trees based on the improved CASA model.

[0105] The aforementioned program can run on a processor or be stored in memory (or a computer-readable medium). Computer-readable media includes both permanent and non-permanent, removable and non-removable media, and information storage can be achieved by any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random-access memory (SRAM), dynamic random-access memory (DRAM), other types of random-access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.

[0106] These computer programs may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes can be implemented by different modules for different steps.

[0107] This embodiment provides such an apparatus or system. The system, referred to as an urban tree net primary productivity estimation system based on an improved CASA model, includes:

[0108] The data acquisition module is used to acquire multi-source geospatial data of the target urban area, wherein the multi-source geospatial data includes high-resolution remote sensing images;

[0109] The parameter calculation module is used to generate the fine-scale photosynthetically active radiation absorption ratio of the target patch based on the high-resolution remote sensing image.

[0110] A light energy utilization rate construction module is used to construct and calculate the actual light energy utilization rate of spatially heterogeneous vegetation, wherein the actual light energy utilization rate responds to the maximum light energy utilization rate of vegetation in response to meteorological stress factors and spatial variations.

[0111] The correction factor construction module is used to construct carbon sequestration correction factors by integrating the structural characteristics of urban trees, soil properties, and landscape pattern indicators.

[0112] The net primary productivity estimation module is used to estimate the net primary productivity of trees in the target urban area based on the fine-scale photosynthetically active radiation absorption ratio, the actual light energy utilization rate, and the carbon sequestration correction factor, using a light energy utilization rate model.

[0113] As one implementation method in this embodiment, the data acquisition module includes:

[0114] The data acquisition unit is used to acquire meteorological data, optical remote sensing data and urban environmental data, wherein the optical remote sensing data includes the high-resolution multispectral image and the coarse-resolution photosynthetically active radiation absorption ratio product.

[0115] The data preprocessing unit is used to perform preprocessing on the meteorological data, the optical remote sensing data, and the urban environmental data, including projection transformation, resampling, and cropping.

[0116] As one implementation method in this embodiment, the parameter calculation module includes:

[0117] An image segmentation unit is used to perform multi-scale segmentation on the high-resolution remote sensing image to form spectrally homogeneous object patches.

[0118] The functional relationship construction unit is used to establish the functional relationship between the average reflectance of object patches of different land cover categories and the photosynthetically active radiation absorption ratio.

[0119] The ratio inversion unit is used to combine the coarse-resolution photosynthetically active radiation absorption ratio product and obtain the fine-scale photosynthetically active radiation absorption ratio at the target patch level through iterative inversion.

[0120] As one implementation method in this embodiment, the light energy utilization construction module includes:

[0121] The maximum light energy utilization calculation unit is used to dynamically calculate the maximum light energy utilization of a spatially continuous distribution based on vegetation index and canopy albedo.

[0122] Temperature stress factor calculation unit, used to calculate temperature stress factor;

[0123] The water stress factor calculation unit is used to calculate the water stress factor using the surface water index.

[0124] The integrated calculation unit is used to multiply the maximum light energy utilization rate of the spatially continuous distribution, the temperature stress factor, and the moisture stress factor to obtain the actual light energy utilization rate.

[0125] As one implementation method in this embodiment, the correction factor construction module includes:

[0126] The indicator selection unit is used to select canopy height, soil bulk density, and Shannon diversity index as input indicators.

[0127] The correction factor calculation unit is used to input the normalized input index into a preset calculation model and output the carbon fixation correction factor.

[0128] As one implementation method in this embodiment, the net primary productivity estimation module includes:

[0129] A photosynthetically active radiation calculation unit is used to calculate the absorbed photosynthetically active radiation based on the total solar radiation and the fine-scale photosynthetically active radiation absorption ratio.

[0130] The productivity calculation unit is used to multiply the absorbed photosynthetically active radiation, the actual light energy utilization rate, and the carbon fixation correction factor to obtain the net primary productivity of the tree.

[0131] The system or apparatus is used to implement the functions of the methods in the above embodiments. Each module in the system or apparatus corresponds to each step in the method, as has been described in the method and will not be repeated here.

[0132] The above implementation method solves the problem of estimating the net primary productivity of urban trees based on the improved CASA model in related technologies, thereby ensuring that the problems existing in the prior art are resolved.

[0133] The above are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for estimating the net primary productivity of urban trees based on an improved CASA model, characterized in that, Includes the following steps: Acquire multi-source geospatial data of the target urban area, wherein the multi-source geospatial data includes high-resolution remote sensing imagery; Based on the high-resolution remote sensing image, generate the fine-scale photosynthetically active radiation absorption ratio for the target patch. Construct and calculate the actual light energy utilization rate of spatially heterogeneous vegetation, wherein the actual light energy utilization rate responds to the maximum light energy utilization rate of vegetation in response to meteorological stress factors and spatial variations; A carbon sequestration correction factor was constructed by integrating the structural characteristics of urban trees, soil properties, and landscape pattern indicators. Based on the fine-scale photosynthetically active radiation absorption ratio, the actual light energy utilization rate, and the carbon sequestration correction factor, the net primary productivity of trees in the target urban area is estimated using a light energy utilization rate model.

2. The method according to claim 1, characterized in that, The process of acquiring multi-source geospatial data includes: Acquire meteorological data, optical remote sensing data, and urban environmental data; The optical remote sensing data includes high-resolution multispectral images and coarse-resolution photosynthetically active radiation absorption ratio products. The meteorological data, the optical remote sensing data, and the urban environmental data are preprocessed, including projection transformation, resampling, and cropping.

3. The method according to claim 1, characterized in that, The process of generating fine-scale photosynthetically active radiation absorptivity (RAPA) for object-oriented patches includes: Multi-scale segmentation is performed on the high-resolution remote sensing image to form spectrally homogeneous object patches; Establish a functional relationship between the average reflectance of object patches of different land cover categories and the photosynthetically active radiation absorption ratio; By combining the coarse-resolution photosynthetically active radiation absorption ratio (RAEP) product, the fine-scale RAEP at the target patch level is obtained through iterative inversion.

4. The method according to claim 1, characterized in that, The process of constructing and calculating the actual light energy utilization rate of spatially heterogeneous vegetation includes: The maximum light energy utilization rate of spatially continuous distribution is dynamically calculated based on vegetation index and canopy albedo. Calculate the temperature stress factor; Calculate water stress factors using the surface water index; The actual light energy utilization rate is obtained by multiplying the maximum light energy utilization rate of the spatially continuous distribution, the temperature stress factor, and the moisture stress factor.

5. The method according to claim 1, characterized in that, The process of constructing the carbon fixation correction factor includes: Canopy height, soil bulk density, and Shannon diversity index were selected as input indicators. The normalized input index is input into a preset calculation model, and the carbon fixation correction factor is output.

6. The method according to claim 1, characterized in that, The process of estimating tree net primary productivity using a light energy utilization model includes: The absorbed photosynthetically active radiation is calculated based on the total solar radiation and the fine-scale photosynthetically active radiation absorption ratio. The net primary productivity of the tree is obtained by multiplying the absorbed photosynthetically active radiation, the actual light energy utilization rate, and the carbon fixation correction factor.

7. A system for estimating the net primary productivity of urban trees based on an improved CASA model, characterized in that, The system for implementing the method of any one of claims 1-6 comprises: The data acquisition module is used to acquire multi-source geospatial data of the target urban area, wherein the multi-source geospatial data includes high-resolution remote sensing images; The parameter calculation module is used to generate the fine-scale photosynthetically active radiation absorption ratio of the target patch based on the high-resolution remote sensing image. A light energy utilization rate construction module is used to construct and calculate the actual light energy utilization rate of spatially heterogeneous vegetation, wherein the actual light energy utilization rate responds to the maximum light energy utilization rate of vegetation in response to meteorological stress factors and spatial variations. The correction factor construction module is used to construct carbon sequestration correction factors by integrating the structural characteristics of urban trees, soil properties, and landscape pattern indicators. The net primary productivity estimation module is used to estimate the net primary productivity of trees in the target urban area based on the fine-scale photosynthetically active radiation absorption ratio, the actual light energy utilization rate, and the carbon sequestration correction factor, using a light energy utilization rate model.

8. A computer terminal device, characterized in that, include: One or more processors; A memory, coupled to the processor, for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors perform the steps of the method as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1-6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1-6.

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