Vegetation recovery trend evaluation method based on remote sensing data

Abstract

The embodiment of the invention discloses a vegetation recovery trend evaluation method based on remote sensing data, and relates to the technical field of remote sensing data analysis, and the method comprises the steps: obtaining a vegetation index remote sensing image set of a to-be-researched region, and generating a start time grid map, an end time grid map and a vegetation growth season length grid map of a vegetation growth season; calculating a vegetation index trend and a vegetation growth season length trend of each pixel unit in a preset period; based on the vegetation index trend and the vegetation growth season length trend, vegetation restoration ecological indexes are obtained through calculation, and a spatial distribution diagram of the vegetation restoration ecological indexes of the to-be-researched area is generated. According to the method, the spatial distribution diagram of the vegetation restoration ecological indexes can be generated according to the vegetation restoration ecological indexes and the corresponding pixel units, so that data support can be provided for related research of areas with significant vegetation restoration effects and key restoration areas, and the method has the advantages of high efficiency, flexibility and accuracy; the method is widely applicable to the fields of ecological restoration and environmental protection.

Description

Technical Field

[0001] This application relates to the technical field of remote sensing data analysis, and particularly to a method for evaluating the vegetation restoration trend based on remote sensing data. Background Art

[0002] Vegetation restoration assessment is a core task in ecological environmental protection and restoration work, providing a scientific basis for evaluating the effectiveness of ecological projects, formulating ecological restoration strategies, and implementing environmental management. With the intensification of global climate change and human activities, the urgency of ecological restoration is increasing continuously, and the accuracy of vegetation restoration assessment directly affects the scientific nature of ecological protection policies. As an efficient, non-contact monitoring means with spatial resolution advantages, remote sensing technology has been widely used in the field of vegetation monitoring. By analyzing remote sensing data, the dynamic changes of vegetation coverage, greenness, and growth status can be monitored.

[0003] Currently, the Normalized Difference Vegetation Index (NDVI) is generally used in remote sensing data processing to reflect the surface vegetation coverage and growth status, and is widely used in vegetation monitoring, ecological assessment, and environmental change research. By reflecting the photosynthetic capacity of plants, NDVI can provide vegetation growth status information at different time and space scales. Trend analysis of multi-year NDVI time series data can generate a trend map of vegetation coverage changes, thereby quantifying the dynamic changes of regional vegetation and providing a quantitative basis for ecological restoration and degradation monitoring.

[0004] However, relying solely on the analysis of a single vegetation index such as NDVI to analyze the changes in vegetation coverage is difficult to comprehensively reveal the complexity of the dynamic characteristics of vegetation during the ecological restoration process, especially in terms of the impact of growing season changes on ecosystem restoration. Although there are currently many vegetation restoration assessment methods based on remote sensing data, most methods focus on the vegetation coverage at a single time point or the changes in specific vegetation indices, and it is difficult to comprehensively reveal the dynamic characteristics of the vegetation growing season. Summary of the Invention

[0005] An embodiment of this application provides a method for evaluating the vegetation restoration trend based on remote sensing data to solve the defects of the above-mentioned related technologies. The technical solution is as follows:

[0006] In a first aspect, an embodiment of this application provides a method for evaluating the vegetation restoration trend based on remote sensing data, including:

[0007] Obtain a set of vegetation index remote sensing images of the area to be studied; the set of vegetation index remote sensing images includes vegetation index remote sensing images arranged in chronological order within a preset period;

[0008] For each vegetation index remote sensing image in the set of vegetation index remote sensing images, calculate the start time and end time of the vegetation growth season for each pixel unit in the corresponding sub-period, and generate a start time raster map and an end time raster map of the vegetation growth season for the corresponding sub-period;

[0009] Generate a vegetation growth season length raster map for the corresponding sub-period based on the start time raster map and end time raster map of each sub-period;

[0010] Calculate the vegetation index trend of each pixel unit within the preset period based on the set of vegetation index remote sensing images, and calculate the vegetation growth season length trend of each pixel unit within the preset period based on the start time raster map, end time raster map, and vegetation growth season length raster map of each sub-period;

[0011] Calculate the vegetation restoration ecological index based on the vegetation index trend and the vegetation growth season length trend, and generate a spatial distribution map of the vegetation restoration ecological index for the area to be studied.

[0012] In an alternative scheme of the first aspect, before calculating the start time and end time of the vegetation growth season for each pixel unit in the corresponding sub-period based on each vegetation index remote sensing image in the set of vegetation index remote sensing images, the method further includes:

[0013] Preprocess the vegetation index remote sensing images in the set of vegetation index remote sensing images, fill in the missing data through interpolation calculation, crop the region of interest for each vegetation index remote sensing image, process each vegetation index remote sensing image to a preset time resolution, and process each vegetation index remote sensing image to a preset spatial resolution through resampling, and output the preprocessed set of vegetation index remote sensing images.

[0014] In an alternative scheme of the first aspect, calculating the start time and end time of the vegetation growth season for each pixel unit in the corresponding sub-period based on each vegetation index remote sensing image in the set of vegetation index remote sensing images, and generating a start time raster map and an end time raster map of the vegetation growth season for the corresponding sub-period, includes:

[0015] Extract the vegetation index time series of each pixel unit in the vegetation index remote sensing image of each sub-period;

[0016] Calculate the first-order derivative time series of the vegetation index of each pixel unit based on the vegetation index time series by the first-order derivative method;

[0017] Determine the moment corresponding to the maximum value of the first derivative based on the time series of the first derivative of the vegetation index, and obtain the start time of the vegetation growth season for each pixel unit; determine the moment corresponding to the minimum value of the first derivative based on the time series of the first derivative of the vegetation index, and obtain the end time of the vegetation growth season for each pixel unit.

[0018] Project the start time of the vegetation growth season of each pixel unit in the vegetation index remote sensing image of each sub-period into the corresponding pixel unit respectively, and obtain the start time raster map of each sub-period; project the end time of the vegetation growth season of each pixel unit in the vegetation index remote sensing image of each sub-period into the corresponding pixel unit respectively, and obtain the end time raster map of each sub-period.

[0019] In an alternative scheme of the first aspect, generating the vegetation growth season length raster map corresponding to each sub-period based on the start time raster map and the end time raster map of each sub-period includes:

[0020] Determine the start time of the vegetation growth season and the end time of the vegetation growth season of each grid based on the start time raster map and the end time raster map of the same sub-period;

[0021] Calculate the difference between the start time of the vegetation growth season and the end time of the vegetation growth season to obtain the vegetation growth season length of the corresponding grid;

[0022] Generate the vegetation growth season length raster map corresponding to each sub-period based on the vegetation growth season length of each grid in the area to be studied in the same sub-period.

[0023] In an alternative scheme of the first aspect, the calculation process of the vegetation index trend and the vegetation growth season length trend includes the following steps:

[0024] Generate the vegetation index time series data of each pixel unit in the area to be studied during the preset period based on the vegetation index remote sensing image set;

[0025] Generate the vegetation growth season length time series data of each pixel unit in the area to be studied during the preset period based on the vegetation growth season length raster map of each sub-period;

[0026] Calculate the significance parameters of each pixel unit respectively based on the differences between every two data in the vegetation index time series data and the vegetation growth season length time series data, and extract the pixel units with significance parameters greater than the preset threshold to generate the significant change area;

[0027] Calculate the slopes of the vegetation index time series data and the vegetation growth season length time series data for each pixel unit in the significant change region respectively, and calculate the vegetation index trend and the vegetation growth season length trend for each pixel unit in the significant change region during the preset period.

[0028] In an alternative embodiment of the first aspect, before calculating the vegetation restoration ecological index based on the vegetation index trend and the vegetation growth season length trend, it further includes:

[0029] Normalize the vegetation index trend and the vegetation growth season length trend in the area to be studied;

[0030] Divide the vegetation index trend of each pixel unit by the absolute value maximum of the vegetation index trend in the area to be studied, and output the normalized vegetation index trend;

[0031] Divide the vegetation growth season length trend of each pixel unit by the absolute value maximum of the vegetation growth season length trend in the area to be studied, and output the normalized vegetation index trend.

[0032] In an alternative embodiment of the first aspect, calculating the vegetation restoration ecological index based on the vegetation index trend and the vegetation growth season length trend includes:

[0033] Calculate the weighted sum of the vegetation index trend and the vegetation growth season length trend of each pixel unit in the area to be studied, and output the vegetation restoration ecological index of each pixel unit. The formula is applied:

[0034] VREI =

[0035] w1*NDVI norm +w2*LOS norm ;

[0036] where VREI is the vegetation restoration ecological index, w1 is the weight of the vegetation index trend, NDVI norm is the vegetation index trend, w2 is the weight of the vegetation growth season length trend, and LOS norm is the vegetation growth season length trend.

[0037] In a second aspect, an embodiment of the present application further provides a device for evaluating the vegetation restoration trend based on remote sensing data, including:

[0038] A data acquisition module for acquiring a set of vegetation index remote sensing images of the area to be studied; the set of vegetation index remote sensing images includes vegetation index remote sensing images arranged in chronological order during a preset period;

[0039] A data processing unit, configured to calculate, based on each vegetation index remote sensing image in the set of vegetation index remote sensing images, the start time and end time of the vegetation growth season for each pixel unit in the corresponding sub-period, and further configured to generate a start time raster map and an end time raster map of the vegetation growth season for the corresponding sub-period;

[0040] The data processing unit is further configured to generate a vegetation growth season length raster map for the corresponding sub-period based on the start time raster map and the end time raster map of each sub-period;

[0041] The data processing unit is further configured to calculate the vegetation index trend of each pixel unit within the preset period based on the set of vegetation index remote sensing images, and further configured to calculate the vegetation growth season length trend of each pixel unit within the preset period based on the start time raster map, the end time raster map, and the vegetation growth season length raster map of each sub-period;

[0042] A data output unit, configured to calculate a vegetation restoration ecological index based on the vegetation index trend and the vegetation growth season length trend, and generate a spatial distribution map of the vegetation restoration ecological index of the area to be studied.

[0043] In a third aspect, an embodiment of the present application further provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the method provided in the first aspect or any implementation manner of the first aspect of the embodiment of the present application is implemented.

[0044] In a fourth aspect, the present application further provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method provided in the first aspect or any implementation manner of the first aspect of the embodiment of the present application is implemented.

[0045] The beneficial effects brought by the technical solutions provided in some embodiments of the present application at least include:

[0046] A method for evaluating the vegetation restoration trend based on remote sensing data provided by an embodiment of the present application can accurately reveal the long-term change trends of regional vegetation coverage and the dynamics of the growing season by performing trend analysis on the time series data of remote sensing images of vegetation indices and the length of the vegetation growing season. Different from the evaluation method that solely relies on vegetation indices in the related art, the present application can more comprehensively reflect the ecological restoration process by taking into account the start time, end time, and length changes of the vegetation growing season, can adapt to the differences in different vegetation types and regional characteristics, and improve the scientific nature and regional adaptability of the evaluation. In addition, the present application can generate a spatial distribution map of the vegetation restoration ecological index based on the vegetation restoration ecological index and the corresponding pixel units, thereby providing data support for relevant research on areas with significant vegetation restoration effects and key restoration areas, and having the advantages of high efficiency, flexibility, and accuracy, and being widely applicable to the fields of ecological restoration and environmental protection. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the present application or the related art, the following will briefly introduce the drawings required for use in the description of the embodiments or the related art. Obviously, the drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0048] Figure 1 It is a schematic flowchart of a method for evaluating the vegetation restoration trend based on remote sensing data provided by an embodiment of the present application;

[0049] Figure 2 It is a schematic diagram of the time series of vegetation indices and the time series of the first derivative of vegetation indices of a method for evaluating the vegetation restoration trend based on remote sensing data provided by an embodiment of the present application;

[0050] Figure 3 It is a schematic diagram of the raster map of the start time of the vegetation growing season, the raster map of the end time of the vegetation growing season, and the raster map of the length of the vegetation growing season of a method for evaluating the vegetation restoration trend based on remote sensing data provided by an embodiment of the present application;

[0051] Figure 4 It is a schematic diagram of the distribution of the vegetation index trend of a method for evaluating the vegetation restoration trend based on remote sensing data provided by an embodiment of the present application;

[0052] Figure 5 It is a schematic diagram of the distribution of the trend of the length of the vegetation growing season of a method for evaluating the vegetation restoration trend based on remote sensing data provided by an embodiment of the present application;

[0053] Figure 6 It is a schematic structural diagram of a device for evaluating the vegetation restoration trend based on remote sensing data provided by an embodiment of the present application;

[0054] Figure 7 It is a schematic structural diagram of a device for evaluating the vegetation restoration trend based on remote sensing data provided by an embodiment of the present application.

[0055] Figure 8 It is a schematic structural diagram of an electronic device provided by an embodiment of the present application. Detailed implementation manners

[0056] To make the objectives, technical solutions and advantages of the present application clearer, the technical solutions in the present application will be clearly and completely described below with reference to the accompanying drawings in the present application. Apparently, the described embodiments are some but not all of the embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative efforts shall fall within the protection scope of the present application.

[0057] The terms "including" and "having" and any variations thereof in the specification and claims of the present application and the above-mentioned accompanying drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or modules is not limited to the listed steps or modules, but optionally further includes steps or modules not listed, or optionally further includes other steps or modules inherent to these processes, methods, products or devices.

[0058] It should be noted that the terms "first" and "second" involved in the present application are only used to distinguish similar objects and do not represent a specific order for the objects. Understandably, "first" and "second" can be interchanged with a specific order or sequence when permitted. It should be understood that the objects distinguished by "first" and "second" can be interchanged under appropriate circumstances so that the embodiments of the present application described here can be implemented in an order other than those described or illustrated here.

[0059] It should be noted that the present application reflects the dynamic changes of the vegetation growing season through parameters such as the start time, end time, and length of the growing season, and further calculates relevant parameters reflecting ecological restoration. Understandably, the extension of the growing season length usually means an increase in vegetation growth time, an extension of the photosynthesis duration, and an enhancement of ecological service functions, which can be used as a key parameter to measure the restoration of ecosystem functions. Therefore, it can avoid the defect that the related technology only focuses on the vegetation cover at a single time point or the change of a specific vegetation index, and can more comprehensively reveal the dynamic characteristics of the vegetation growing season.

[0060] The present application will be described in detail below with reference to specific embodiments.

[0061] Next, in combination with Figure 1, introduce a method for evaluating the vegetation restoration trend based on remote sensing data provided by the embodiments of the present application. For details, please refer to Figure 1 , Figure 1 shows a schematic flowchart of a method for evaluating the vegetation restoration trend based on remote sensing data provided by the embodiments of the present application. As Figure 1 shown, the method includes the following steps:

[0062] S101, obtain a set of vegetation index remote sensing images of the area to be studied;

[0063] S102, respectively calculate the start time and end time of the vegetation growth season of each pixel unit in the corresponding sub-period based on each vegetation index remote sensing image in the set of vegetation index remote sensing images, and generate a start time raster map and an end time raster map of the vegetation growth season of the corresponding sub-period;

[0064] S103, generate a vegetation growth season length raster map of the corresponding sub-period based on the start time raster map and the end time raster map of each sub-period;

[0065] S104, calculate the vegetation index trend of each pixel unit within the preset period based on the set of vegetation index remote sensing images, and calculate the vegetation growth season length trend of each pixel unit within the preset period based on the start time raster map, the end time raster map and the vegetation growth season length raster map of each sub-period;

[0066] S105, calculate the vegetation restoration ecological index based on the vegetation index trend and the vegetation growth season length trend, and generate a spatial distribution map of the vegetation restoration ecological index of the area to be studied.

[0067] Specifically, in S101, any city or any divided area can be selected as the area to be studied, and the embodiments of the present application do not limit this. The set of vegetation index remote sensing images obtained includes vegetation index remote sensing images arranged in chronological order within the preset period. For example, the area to be studied can be selected as City A, and the preset period can be selected as 2000 - 2020. Specifically, the NDVI images of City A every 16 days within 2000 - 2020 can be obtained.

[0068] In some embodiments, before S102, the vegetation index remote sensing images in the set of vegetation index remote sensing images can be preprocessed. The missing data caused by factors such as cloud shadows can be filled by interpolation calculation, and the region of interest of each vegetation index remote sensing image can be cropped. Each vegetation index remote sensing image is processed to the preset time resolution, and each vegetation index remote sensing image is processed to the preset spatial resolution by resampling, and the preprocessed set of vegetation index remote sensing images is output; for example, the time resolution can be preset to 16 days and the spatial resolution can be preset to 250m.

[0069] Specifically, in S102, the vegetation index time series of each pixel unit in the remote sensing image of the vegetation index for each sub-period can be extracted;

[0070] Based on the vegetation index time series, the first-order derivative time series of the vegetation index of each pixel unit is calculated by the first-order derivative method;

[0071] Based on the first-order derivative time series of the vegetation index, the moment corresponding to the maximum value of the first-order derivative is determined to obtain the start time of the vegetation growth season for each pixel unit; based on the first-order derivative time series of the vegetation index, the moment corresponding to the minimum value of the first-order derivative is determined to obtain the end time of the vegetation growth season for each pixel unit.

[0072] Exemplarily, taking one year as a sub-period and taking the change of the vegetation index of a certain pixel unit over time within one year as an example, the vegetation index time series and the calculated first-order derivative time series of the vegetation index are as Figure 2 shown. The horizontal axis is the day of the year (DOY); the DOY corresponding to the maximum value of the first-order derivative is extracted to obtain the start time of the vegetation growth season (Start of the growing season, SOS) for each pixel unit, and the DOY corresponding to the minimum value of the first-order derivative is extracted to obtain the end time of the vegetation growth season (End of the growing season, EOS) for each pixel unit.

[0073] Furthermore, the start time of the vegetation growth season of each pixel unit in the remote sensing image of the vegetation index for each sub-period is projected into the corresponding pixel unit respectively to obtain the start time raster map for each sub-period; the end time of the vegetation growth season of each pixel unit in the remote sensing image of the vegetation index for each sub-period is projected into the corresponding pixel unit respectively to obtain the end time raster map for each sub-period.

[0074] Exemplarily, the start time SOS raster image and the end time EOS raster image of the vegetation growth season in City A from 2000 to 2020 can be calculated, as Figure 3 shown. Taking 2005, 2010, 2015, and 2020 as examples respectively, the spatial distribution of the start time SOS, the end time EOS, and the length of the vegetation growth season (Length of the growing season, LOS) in City A is exemplified, and the corresponding moments can be represented by different colors. For example Figure 3As shown in the distribution map of the start of the vegetation growth season (SOS) in 2005 in China, the red grids indicate that the start of the vegetation growth season within the corresponding grids is within the 30th to 90th days of 2005. The embodiments of the present application do not limit this.

[0075] Specifically, in S103, the start time of the vegetation growth season and the end time of the vegetation growth season of each grid can be determined based on the start time grid map and the end time grid map of the same sub-period;

[0076] The difference between the start time of the vegetation growth season and the end time of the vegetation growth season is calculated to obtain the vegetation growth season length of the corresponding grid;

[0077] Based on the vegetation growth season lengths of each grid in the area to be studied in the same sub-period, a vegetation growth season length grid map of the corresponding sub-period is generated.

[0078] Exemplarily, for example Figure 3 As shown in the spatial distribution map of the length of the vegetation growth season (LOS) in 2005 in China, the orange grids indicate that the vegetation growth season length within the corresponding grids is within 150 - 180 days. The embodiments of the present application do not limit this.

[0079] Exemplarily, as Figure 3 shown, taking 2005, 2010, 2015, and 2020 as examples, the spatial distribution of the start of the vegetation growth season (SOS), the end of the vegetation growth season (EOS), and the length (LOS) in City A is presented. It can be seen that there is an obvious change trend in the vegetation growth season in City A from 2000 to 2020. In 2005, most of the vegetation in City A started to grow in the first and middle ten days of April and ended in the last ten days of October and the first ten days of November, with a duration of mostly 5 - 7 months; in 2010, 75% of the vegetation in City A grew in the last ten days of April, which was 15 days later than in 2005, and the end time of growth was also postponed by 15 days. Therefore, in 2010, the start and end times of the vegetation growth in City A (such as the forest land and grassland in the northwest) were postponed by half a month, and the growth season duration still concentrated in 6 - 7 months. In 2015, 60% of the vegetation in City A started to grow in the first and middle ten days of April, and 20% of the vegetation started to sprout and grow before March. Compared with 2010, the vegetation growth was significantly advanced. The end time of the vegetation growth still concentrated between October 16th and November 15th, resulting in a longer vegetation growth duration, and more than 25% of the vegetation grew for more than 7 months. In 2020, a larger area of the vegetation showed a phenomenon of advanced growth, with more than 80% of the vegetation starting to sprout and turn green before the middle of April, and the end time of the vegetation growth still remained near November, thus extending the vegetation growth season duration in some areas, and almost 40% of the vegetation areas grew for more than 7 months, especially the cultivated land in the east and south.

[0080] Specifically, in S104, the calculation processes of the vegetation index trend and the vegetation growing season length trend include the following steps:

[0081] Generate the vegetation index time series data of each pixel unit in the area to be studied during the preset period based on the set of remote sensing images of the vegetation index;

[0082] Generate the vegetation growing season length time series data of each pixel unit in the area to be studied during the preset period based on the raster map of the vegetation growing season length for each sub-period;

[0083] Calculate the significance parameter of each pixel unit respectively based on the difference between every two data in the vegetation index time series data and the vegetation growing season length time series data, and extract the pixel units with the significance parameter greater than the preset threshold to generate the significant change area;

[0084] Calculate the slopes of the vegetation index time series data and the vegetation growing season length time series data of each pixel unit in the significant change area respectively, and calculate the vegetation index trend and the vegetation growing season length trend of each pixel unit in the significant change area during the preset period.

[0085] In some embodiments, during the calculation processes of the vegetation index trend and the vegetation growing season length trend, the vegetation index time series data and the vegetation growing season length time series data can be expressed as {x1, x2, …, xn}, where n is the length of the time series. For each pair of data (xi, xj), where 1 ≤ i < j ≤ n, calculate the difference sign between each pair of data:

[0086] If xj > xi, the difference sign is +1 (indicating an upward trend);

[0087] If xj < xi, the difference sign is -1 (indicating a downward trend);

[0088] If xj = xi, the difference sign is 0 (indicating no trend);

[0089] Calculate the MK statistic S, which is the sum of the trend sign sequence:

[0090] S = ∑ 1≤i＜j≤n sign(x j - x i );

[0091] Calculate the standardized value Z of the MK statistic S, and Z is also the significance parameter:

[0092]

[0093] Among them, the variance Var(S) is calculated by the following formula:

[0094]

[0095] According to the magnitude of the Z value, the significance of the trend can be judged. If |Z| is greater than the critical value under the standard normal distribution (usually 1.96, corresponding to a significance level of 0.05), it is considered that there is a significant upward or downward trend in this area, and then the significant change area of the vegetation change in the corresponding period within the area to be studied is determined.

[0096] In some embodiments, during the calculation of the vegetation index trend and the vegetation growing season length trend, the slopes of the vegetation index time series data and the vegetation growing season length time series data of each pixel unit within the significant change area are calculated respectively, specifically including:

[0097] To further quantify the change rate of the trend, the Sen Slope method can be used to calculate the slope Q of the time series data. Sen Slope is a median-based trend estimation method, applicable to data with irregular fluctuations, and can provide a quantitative result of the trend change. The calculation formula of Sen Slope is as follows:

[0098]

[0099] Among them, Q is the slope of the time series, representing the change amount per unit time. The slope of the trend is determined by calculating the difference between each pair of data points and finding the median of all differences. Through this method, the obtained slope value Q can be used to measure the change rate of the regional vegetation index and the vegetation growing season length.

[0100] If Q>0, it means that the time series has an upward trend, that is, the vegetation is recovering;

[0101] If Q<0, it means that the time series has a downward trend, that is, the vegetation is deteriorating;

[0102] If Q = 0, it means that the time series has no obvious change trend.

[0103] In some embodiments, in order to ensure the comparison of different indicators on the same scale, before calculating the vegetation recovery ecological index based on the vegetation index trend and the vegetation growing season length trend in S106, the vegetation index trend and the vegetation growing season length trend within the area to be studied can be normalized;

[0104] Divide the vegetation index trend of each pixel unit by the absolute value maximum of the vegetation index trend within the area to be studied, and output the normalized vegetation index trend;

[0105] Divide the vegetation growth season length trend of each pixel unit by the maximum absolute value of the vegetation growth season length trend within the area to be studied, and output the vegetation index trend after normalization.

[0106] Specifically, by dividing the trend value of each pixel unit by the maximum value of the absolute value within the area to be studied, the value of the result after normalization is within the range of [-1, 1]. Apply the formula:

[0107]

[0108] where X represents the value of the vegetation index trend of the vegetation index or the value of the vegetation growth season length trend. X norm represents the value of the vegetation index trend after normalization or the value of the vegetation growth season length trend.

[0109] Exemplarily, the distribution of the calculated vegetation index trend or vegetation growth season length trend is respectively as Figure 4 and Figure 5 shown. It can be seen that the trends of the vegetation index NDVI and the vegetation growth season length LOS both show a significant decreasing trend in the southeast region of City A, and the vegetation in this area is mainly cultivated land. In contrast, the vegetation index NDVI and the growth season length LOS of the forest land and grassland in the west and north show an obvious increasing trend, that is, the vegetation in the west and north of City A shows an obvious recovery state during 2000 - 2020, and the ecological greening effect is remarkable. Compared with the trend of NDVI, the vegetation growth season length LOS shows the vegetation greening effect in a wider area, which also reflects that the vegetation growth season can show a more accurate assessment of the ecological effect of vegetation restoration. When measuring vegetation restoration, although NDVI can reflect the change of vegetation coverage, it fails to comprehensively capture the duration of the growth season and the overall situation of ecological restoration. While LOS not only considers the change of vegetation coverage, but also reflects the length of the growth season, directly related to the photosynthesis time, biomass accumulation and enhancement of ecological functions.

[0110] Specifically, in S105, the vegetation restoration ecological index is calculated based on the vegetation index trend and the vegetation growth season length trend, specifically including:

[0111] Calculate the weighted sum of the vegetation index trend and the vegetation growth season length trend of each pixel unit within the area to be studied, and output the vegetation restoration ecological index of each pixel unit. Apply the formula:

[0112] VREI =

[0113] w1 * NDVI norm + w2 * LOS norm ;

[0114] Among them, VREI is the vegetation restoration ecological index, w1 is the weight of the vegetation index trend, NDVI norm is the vegetation index trend, w2 is the weight of the vegetation growing season length trend, LOS norm is the vegetation growing season length trend.

[0115] Exemplarily, the vegetation restoration ecological index (VREI) can be calculated based on Python, and combined with ARCGIS for hierarchical visualization and area ratio statistics. The spatial distribution map of the vegetation restoration ecological index of the area to be studied is as shown in Figure 6 the figure. The analysis results show that about 1.8% of the vegetation restoration ecological index in City A is in the range of -1 to -0.2, indicating that the vegetation ecosystem in these areas is in a significant deterioration stage, mainly distributed in the cultivated land areas in the south of City A. In addition, 4.9% of the vegetation is in a slight deterioration stage (the vegetation restoration ecological index value is in the range of -0.2 to 0), and these areas are mainly distributed in the cultivated land around the urban area of City A. At the same time, about 28.2% of the vegetation areas showed a slight greening trend in the past 20 years, mainly distributed in the forest areas in the northwest of City A. The most significant finding is that 65.1% of the vegetation was in a significant ecological restoration stage between 2000 and 2020, and the vegetation restoration ecological index value exceeded 0.2, mainly concentrated in the east and southwest of City A. The vegetation in these areas not only showed a significant increasing trend in NDVI, but also the vegetation growing season length (LOS) was significantly extended. The extension of the growing season means an increase in the vegetation photosynthesis time, which promotes the accumulation of plant biomass and the improvement of ecosystem service functions, further promoting the ecological restoration process in these areas.

[0116] The following is the device embodiment of the present application, which can be used to execute the method embodiment of the present application. For the details not disclosed in the device embodiment of the present application, please refer to the method embodiment of the present application.

[0117] Next, please refer to Figure 7 , which is a schematic structural diagram of a device for evaluating the vegetation restoration trend based on remote sensing data provided by an exemplary embodiment of the present application. The device can be implemented as all or part of a terminal through software, hardware or a combination of both, and can also be integrated as an independent module on a server. A device for evaluating the vegetation restoration trend based on remote sensing data in the embodiment of the present application can be applied to a terminal or the cloud. The device 70 includes a data acquisition module 701, a data processing unit 702 and a data output unit 703, where:

[0118] The data acquisition module 701 is used to acquire a set of remote sensing images of the vegetation index of the area to be studied; the set of remote sensing images of the vegetation index includes remote sensing images of the vegetation index arranged in chronological order within a preset period;

[0119] The data processing unit 702 is used to calculate the start time and end time of the vegetation growth season for each pixel unit in the corresponding sub-period based on each vegetation index remote sensing image in the set of vegetation index remote sensing images, and is also used to generate a start time raster map and an end time raster map of the vegetation growth season for the corresponding sub-period;

[0120] The data processing unit 702 is further used to generate a vegetation growth season length raster map for the corresponding sub-period based on the start time raster map and the end time raster map of each sub-period;

[0121] The data processing unit 702 is further used to calculate the vegetation index trend of each pixel unit within the preset period based on the set of vegetation index remote sensing images, and is also used to calculate the vegetation growth season length trend of each pixel unit within the preset period based on the start time raster map, the end time raster map, and the vegetation growth season length raster map of each sub-period;

[0122] The data output unit 703 is used to calculate the vegetation restoration ecological index based on the vegetation index trend and the vegetation growth season length trend, and generate a spatial distribution map of the vegetation restoration ecological index of the area to be studied.

[0123] It should be noted that when the device 70 provided in the above embodiment executes a method for evaluating the vegetation restoration trend based on remote sensing data, only the above-mentioned division of each functional module is used for illustration. In practical applications, the above functions can be allocated to different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device provided in the above embodiment and an embodiment of a method for evaluating the vegetation restoration trend based on remote sensing data belong to the same concept, and the implementation process is detailed in the method embodiment, which will not be repeated here.

[0124] The embodiment of the present application further provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the steps of the method in any of the above embodiments.

[0125] Please refer to Figure 8 , which is a structural block diagram of an electronic device provided by an embodiment of the present application.

[0126] As Figure 8 shown, the electronic device 800 includes: a processor 801 and a memory 802.

[0127] In the embodiments of the present application, the processor 801 is the control center of the computer system, which can be the processor of a physical machine or the processor of a virtual machine. The processor 801 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 801 may be implemented in at least one of the following hardware forms: DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array).

[0128] The processor 801 may also include a main processor and a coprocessor. The main processor is a processor used to process data in the wake state, also known as the CPU (Central Processing Unit); the coprocessor is a low-power processor used to process data in the standby state.

[0129] The memory 802 may include one or more computer-readable storage media, and the computer-readable storage media may be non-transitory. The memory 802 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices and flash storage devices. In some embodiments of the present application, the non-transitory computer-readable storage medium in the memory 802 is used to store at least one instruction, and the at least one instruction is used to be executed by the processor 801 to implement the method in the embodiments of the present application.

[0130] In some embodiments, the electronic device 800 further includes: a peripheral device interface 803 and at least one peripheral device 804. The processor 801, the memory 802, and the peripheral device interface 803 may be connected through a bus or signal lines. Each peripheral device 804 may be connected to the peripheral device interface 803 through a bus, signal lines, or a circuit board. Specifically, the peripheral device 804 includes: a display screen, a camera, and an audio circuit. The peripheral device interface 803 may be used to connect at least one peripheral device related to I / O (Input / Output) to the processor 801 and the memory 802.

[0131] In some embodiments of the present application, the processor 801, the memory 802, and the peripheral device interface 803 are integrated on the same chip or circuit board; in some other embodiments of the present application, any one or two of the processor 801, the memory 802, and the peripheral device interface 803 may be implemented on a separate chip or circuit board. The embodiments of the present application do not make specific limitations on this.

[0132] The block diagram of the electronic device shown in the embodiments of the present application does not limit the electronic device 800. The electronic device 800 may include more or fewer components than those shown in the figure, combine some components, or adopt different component arrangements.

[0133] The embodiments of the present application also provide a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the steps of the method in any of the foregoing embodiments are implemented. Among them, the computer-readable storage medium may include, but is not limited to, any type of disk, including floppy disks, optical disks, DVDs, CD-ROMs, microdrives, and magneto-optical disks, ROMs, RAMs, EPROMs, EEPROMs, DRAMs, VRAMs, flash memory devices, magnetic cards or optical cards, nanosystems (including molecular memory ICs), or any type of medium or device suitable for storing instructions and / or data.

[0134] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the essence of the above technical solution, or the part that contributes to the related technology, can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disks, optical disks, etc., and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0135] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, and are not intended to limit them. Although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features. And these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A vegetation restoration trend assessment method based on remote sensing data, characterized in that: include: Obtain a collection of remote sensing images of vegetation indices in the area to be studied; The vegetation index remote sensing image set includes vegetation index remote sensing images arranged in chronological order within a preset period; Based on each vegetation index remote sensing image in the vegetation index remote sensing image set, the start time of the vegetation growth season and the end time of the vegetation growth season of each pixel unit in the corresponding sub-period are calculated respectively, and the start time grid map and the end time grid map of the vegetation growth season of the corresponding sub-period are generated; Generate a vegetation growth season length grid map of the corresponding sub-period based on the start time grid map and the end time grid map of each sub-period; Calculating the vegetation index trend of each pixel unit in the preset period based on the vegetation index remote sensing image set, and calculating the vegetation growth season length trend of each pixel unit in the preset period based on the start time grid map, the end time grid map and the vegetation growth season length grid map of each sub-period; A vegetation restoration ecological index is calculated based on the vegetation index trend and the vegetation growing season length trend, and a spatial distribution map of the vegetation restoration ecological index of the area to be studied is generated.

2. The method according to claim 1, characterized in that: The method further comprises: calculating the vegetation growth season start time and vegetation growth season end time of each pixel unit in the corresponding sub-period based on each vegetation index remote sensing image in the vegetation index remote sensing image set respectively. The vegetation index remote sensing images in the vegetation index remote sensing image set are preprocessed, missing data are filled by interpolation calculation, and the area of ​​interest is cropped for each vegetation index remote sensing image, each vegetation index remote sensing image is processed to a preset time resolution, each vegetation index remote sensing image is processed to a preset spatial resolution by resampling, and the preprocessed vegetation index remote sensing image set is output.

3. The method according to claim 1 or 2, characterized in that: The method comprises: calculating the start time and end time of the vegetation growth season of each pixel unit in the corresponding sub-period based on each vegetation index remote sensing image in the vegetation index remote sensing image set, and generating a start time grid map and an end time grid map of the vegetation growth season of the corresponding sub-period, including: Extract the vegetation index time series of each pixel unit in the vegetation index remote sensing image of each sub-period; Calculating the first-order derivative time series of the vegetation index of each pixel unit based on the vegetation index time series by a first-order derivative method; Determine the time corresponding to the maximum value of the first-order derivative value based on the time series of the first-order derivative of the vegetation index, and obtain the start time of the vegetation growth season of each pixel unit; determine the time corresponding to the minimum value of the first-order derivative value based on the time series of the first-order derivative of the vegetation index, and obtain the end time of the vegetation growth season of each pixel unit; The start time of the vegetation growing season of each pixel unit in the vegetation index remote sensing image of each sub-period is projected to the corresponding pixel unit to obtain the start time grid map of each sub-period; the end time of the vegetation growing season of each pixel unit in the vegetation index remote sensing image of each sub-period is projected to the corresponding pixel unit to obtain the end time grid map of each sub-period.

4. The method according to claim 3, characterized in that The step of generating a vegetation growth season length grid map of a corresponding sub-period based on the start time grid map and the end time grid map of each sub-period comprises: Determine the start time and end time of the vegetation growing season of each grid based on the start time grid map and the end time grid map of the same sub-period; The length of the vegetation growing season of the corresponding grid is obtained by calculating the difference between the start time of the vegetation growing season and the end time of the vegetation growing season; A grid map of the length of the vegetation growing season of the corresponding sub-period is generated based on the length of the vegetation growing season of each grid in the area to be studied in the same sub-period.

5. The method according to claim 1, characterized in that The calculation process of the vegetation index trend and the vegetation growing season length trend comprises the following steps: Generate vegetation index time series data of each pixel unit in the area to be studied within the preset period based on the vegetation index remote sensing image set; Generate vegetation growth season length time series data of each pixel unit in the area to be studied within the preset period based on the vegetation growth season length grid map of each sub-period; Calculating the significance parameter of each pixel unit based on the difference between each two data in the vegetation index time series data and the vegetation growth season length time series data, extracting the pixel units whose significance parameters are greater than a preset threshold to generate a significant change area; The slopes of the vegetation index time series data and the vegetation growing season length time series data of each pixel unit in the significant change area are calculated respectively, and the vegetation index trend and the vegetation growing season length trend of each pixel unit in the significant change area during the preset period are calculated.

6. The method according to claim 5, characterized in that Before calculating the vegetation restoration ecological index based on the vegetation index trend and the vegetation growth season length trend, the method further includes: Normalizing the trend of vegetation index and the trend of vegetation growing season length in the area to be studied; Dividing the vegetation index trend of each pixel unit by the maximum absolute value of the vegetation index trend in the area to be studied, and outputting a normalized vegetation index trend; The vegetation growing season length trend of each pixel unit is divided by the absolute maximum value of the vegetation growing season length trend in the area to be studied, and a normalized vegetation index trend is output.

7. The method according to claim 1, characterized in that The vegetation restoration ecological index calculated based on the vegetation index trend and the vegetation growing season length trend includes: Calculate the weighted sum of the vegetation index trend and the vegetation growing season length trend of each pixel unit in the study area, output the vegetation restoration ecological index of each pixel unit, and apply the formula: VREI= w1*NDVI norm +w2*LOS norm ; Among them, VREI is the vegetation restoration ecological index, w1 is the weight of the vegetation index trend, NDVI is norm is the trend of vegetation index, w2 is the weight of the trend of vegetation growing season length, LOS norm is the trend of vegetation growing season length.

8. A vegetation restoration trend assessment device based on remote sensing data, characterized in that: include: The data acquisition module is used to obtain a set of remote sensing images of vegetation indices in the area to be studied; The vegetation index remote sensing image set includes vegetation index remote sensing images arranged in chronological order within a preset period; A data processing unit, used to calculate the start time and end time of the vegetation growth season of each pixel unit in the corresponding sub-period based on each vegetation index remote sensing image in the vegetation index remote sensing image set, and also used to generate a start time grid map and an end time grid map of the vegetation growth season of the corresponding sub-period; The data processing unit is also used to generate a vegetation growth season length grid map of the corresponding sub-period based on the start time grid map and the end time grid map of each sub-period; The data processing unit is further used to calculate the vegetation index trend of each pixel unit in the preset period based on the vegetation index remote sensing image set, and is also used to calculate the vegetation growth season length trend of each pixel unit in the preset period based on the start time grid map, the end time grid map and the vegetation growth season length grid map of each sub-period; A data output unit is used to calculate the vegetation restoration ecological index based on the vegetation index trend and the vegetation growing season length trend, and generate a spatial distribution map of the vegetation restoration ecological index of the area to be studied.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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