Sequential remote sensing vegetation monitoring method for ecological restoration of grassland desertification

Abstract

The invention provides a grassland desertification ecological restoration time sequence remote sensing vegetation monitoring method, and relates to the technical field of remote sensing and vegetation monitoring, and the method comprises the steps: collecting the image data of a red light wave band and a near-infrared wave band of a target region; performing data preprocessing on the image data to obtain preprocessed data; the NDVI value of each pixel in the preprocessed data is calculated; optimizing the NDVI value by using Gaussian kernel transformation to obtain a kNDVI value; and based on a time sequence analysis method, according to the kNDVI value, analyzing the vegetation coverage change trend of the target area. The invention not only provides a scientific and effective vegetation monitoring method, but also shows wide application potential in environmental monitoring and ecological restoration through practical application. According to the invention, the traditional NDVI is improved through a kernel method, so that the application of the NDVI in a complex environment is more accurate and reliable.

Description

Technical Field

[0001] The invention relates to the technical field of remote sensing and vegetation monitoring, and in particular to a time-series remote sensing vegetation monitoring method for grassland desertification ecological restoration. Background Art

[0002] Grassland desertification is one of the major ecological problems facing the world, affecting land productivity and biodiversity. Although traditional remote sensing monitoring methods such as NDVI are widely used in global vegetation monitoring, their accuracy is limited in desertified areas due to the high soil background reflectivity. Although indices such as EVI have improved the defects of NDVI, they still have limitations in extreme environments. Therefore, a monitoring method that can more accurately reflect the status of vegetation and the progress of ecological restoration is needed. Summary of the invention

[0003] In order to overcome the deficiencies of the prior art, the purpose of the present invention is to provide a time-series remote sensing vegetation monitoring method for grassland desertification ecological restoration, which can significantly improve the accuracy and sensitivity of vegetation monitoring.

[0004] To achieve the above object, the present invention provides the following solutions:

[0005] A method for monitoring grassland desertification ecological restoration using time-series remote sensing vegetation, comprising:

[0006] Collect image data of red light band and near infrared band of target area;

[0007] Performing data preprocessing on the image data to obtain preprocessed data;

[0008] Calculating the NDVI value of each pixel in the preprocessed data;

[0009] Applying Gaussian kernel transformation to optimize the NDVI value to obtain a kNDVI value;

[0010] Based on the time series analysis method, the vegetation coverage change trend of the target area is analyzed according to the kNDVI value.

[0011] Preferably, collecting image data of the target area in the red light band and the near infrared band includes:

[0012] Determining the collection frequency according to the growth cycle and seasonal changes of the vegetation in the target area;

[0013] Based on the acquisition frequency, image data in the red light band and the near infrared band are determined using a satellite equipped with a high-resolution multispectral sensor.

[0014] Preferably, the data preprocessing methods for the image data include: radiation correction, atmospheric correction and cloud removal.

[0015] Preferably, the software for preprocessing the image data is ENVI or ERDAS.

[0016] Preferably, the calculation formula of the kNDVI value is:

[0017] kNDVI=tanh(NDVI 2 )

[0018] Among them, kNDVI is the kNDVI value, and NDVI is the NDVI value.

[0019] Preferably, the calculation formula of the kNDVI value is:

[0020]

[0021] Wherein, kNDVI is the kNDVI value, σ is the length scale parameter specified in each specific application, NIR is the near infrared band data in the preprocessed data; red is the red light band data in the preprocessed data.

[0022] Preferably, the time series analysis method is a dynamic time warping algorithm.

[0023] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0024] The present invention provides a method for monitoring vegetation by remote sensing in a time series for grassland desertification ecological restoration, comprising: collecting image data of a target area in a red light band and a near infrared band; performing data preprocessing on the image data to obtain preprocessed data; calculating the NDVI value of each pixel in the preprocessed data; optimizing the NDVI value by applying a Gaussian kernel transform to obtain a kNDVI value; and analyzing the vegetation coverage change trend of the target area according to the kNDVI value based on a time series analysis method. The present invention not only provides a scientific and effective vegetation monitoring method, but also demonstrates its wide application potential in environmental monitoring and ecological restoration through practical applications. The present invention improves the traditional NDVI by a kernel method, making its application in complex environments more accurate and reliable. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0026] Figure 1 A flow chart of a method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0027] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0028] The purpose of the present invention is to provide a grassland desertification ecological restoration time series remote sensing vegetation monitoring method, which improves the traditional NDVI through the kernel method, making its application in complex environments more accurate and reliable.

[0029] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0030] Figure 1 A flow chart of a method provided by an embodiment of the present invention, such as Figure 1 As shown, the present invention provides a method for monitoring grassland desertification ecological restoration by remote sensing vegetation in a time series, comprising:

[0031] Step 100: collecting image data of the target area in the red light band and the near infrared band;

[0032] Step 200: performing data preprocessing on the image data to obtain preprocessed data;

[0033] Step 300: Calculate the NDVI value of each pixel in the preprocessed data;

[0034] Step 400: applying Gaussian kernel transformation to optimize the NDVI value to obtain a kNDVI value;

[0035] Step 500: Based on a time series analysis method, the vegetation coverage change trend of the target area is analyzed according to the kNDVI value.

[0036] Specifically, step 100 of this embodiment is data collection, using a satellite equipped with a high-resolution multispectral sensor to ensure that necessary spectral data including red and near-infrared (NIR) bands can be captured. The frequency and timing of collection are specifically: according to the growth cycle and seasonal changes of vegetation, data collection is performed regularly, such as once a month, to ensure the timeliness and continuity of monitoring data.

[0037] Optionally, this embodiment performs additional collection after a special weather event to evaluate its impact on the state of vegetation.

[0038] Optionally, step 200 of this embodiment is data preprocessing, which specifically includes:

[0039] Radiometric correction: Corrects for the sensor's systematic errors and the effects of varying lighting conditions.

[0040] Atmospheric correction: Eliminate the effects of atmospheric scattering and absorption on optical signals and improve data authenticity.

[0041] Cloud removal: Use cloud detection algorithms to exclude cloud-covered areas in images to prevent distortion of data analysis results.

[0042] Specifically, this embodiment applies professional remote sensing software (such as ENVI, ERDAS) for preprocessing and uses advanced algorithms to ensure data quality.

[0043] Furthermore, step 300 and step 400 of this embodiment are as follows:

[0044] 1. kNDVI calculation:

[0045] Application of Kernel Methods:

[0046] Calculates the traditional NDVI value for each pixel.

[0047] Gaussian kernel transformation is applied to optimize the NDVI value, and the formula is:

[0048]

[0049] Where: σ is a length scale parameter specified in each specific application, indicating the sensitivity of the index to sparse / dense vegetation areas; NIR is the near infrared band; red is the red band; tanh represents the hyperbolic tangent function. A reasonable choice is to take the average value σ = 0.5 (NIR-red), in which case the formula for kNDVI is as follows:

[0050] kNDVI=tanh(NDVI 2 ) (2)

[0051] Furthermore, step 500 of this embodiment is: data analysis and application, and the specific steps are as follows:

[0052] Use time series analysis methods, such as dynamic time warping (DTW), to analyze vegetation cover change trends.

[0053] Combine statistical methods to evaluate vegetation health and ecological restoration effects.

[0054] Through the detailed description of the above steps, the invention of the kNDVI index not only provides a scientific and effective vegetation monitoring method, but also demonstrates its wide application potential in environmental monitoring and ecological restoration through practical applications. This method improves the traditional NDVI through the kernel method, making its application in complex environments more accurate and reliable.

[0055] The beneficial effects of the present invention are as follows:

[0056] The present invention provides a remote sensing vegetation monitoring method for grassland desertification ecological restoration based on the kernel normalized difference vegetation index (kNDVI), which effectively improves and optimizes the limitations of traditional vegetation monitoring technology in complex environments. By introducing the kernel method to process the traditional NDVI value, the present invention not only significantly improves the accuracy and sensitivity of vegetation monitoring, but also enhances the data processing capability, providing a new scientific tool for grassland desertification control and ecological restoration.

[0057] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0058] The principles and implementation methods of the present invention are described in this article using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for monitoring grassland desertification ecological restoration by remote sensing vegetation in a time series, characterized in that: include: Collect image data of red light band and near infrared band of target area; Performing data preprocessing on the image data to obtain preprocessed data; Calculating the NDVI value of each pixel in the preprocessed data; Applying Gaussian kernel transformation to optimize the NDVI value to obtain a kNDVI value; Based on the time series analysis method, the vegetation coverage change trend of the target area is analyzed according to the kNDVI value.

2. The method for monitoring grassland desertification ecological restoration by remote sensing vegetation in time series according to claim 1, characterized in that: Collect image data of the red light band and near infrared band of the target area, including: Determining the collection frequency according to the growth cycle and seasonal changes of the vegetation in the target area; Based on the acquisition frequency, image data in the red light band and the near infrared band are determined using a satellite equipped with a high-resolution multispectral sensor.

3. The method for monitoring grassland desertification ecological restoration by remote sensing vegetation in time series according to claim 1, characterized in that: The method of preprocessing the image data includes: radiation correction, atmospheric correction and cloud removal.

4. The method for monitoring grassland desertification ecological restoration by remote sensing vegetation in time series according to claim 1, characterized in that: The software for preprocessing the image data is ENVI or ERDAS.

5. The method for monitoring grassland desertification ecological restoration by remote sensing vegetation in time series according to claim 1, characterized in that: The calculation formula of the kNDVI value is: kNDVI=tanh(NDVI 2 ) Among them, kNDVI is the kNDVI value, and NDVI is the NDVI value.

6. The method for monitoring grassland desertification ecological restoration by remote sensing vegetation in time series according to claim 1, characterized in that: The calculation formula of the kNDVI value is: Wherein, kNDVI is the kNDVI value, σ is the length scale parameter specified in each specific application, NIR is the near infrared band data in the preprocessed data; red is the red light band data in the preprocessed data.

7. The method for monitoring grassland desertification ecological restoration by remote sensing vegetation in time series according to claim 1, characterized in that: The time series analysis method is a dynamic time warping algorithm.

Images