A method for evaluating the landscape differentiation characteristics of phyllostachys edulis forests based on size years

Abstract

The application discloses a kind of based on size year's Phyllostachys edulis forest landscape differentiation feature evaluation method, comprising the following steps: obtaining regional boundary vector data and remote sensing image data;Based on remote sensing image data, obtain regional Phyllostachys edulis forest size year classification data and Phyllostachys edulis forest size year distribution area;Based on Phyllostachys edulis forest size year distribution area, create new type Phyllostachys edulis forest size year differentiation index;Based on Phyllostachys edulis forest size year differentiation index, the grade of Phyllostachys edulis forest size year differentiation is divided;Based on Phyllostachys edulis forest size year differentiation index, the landscape differentiation feature of Phyllostachys edulis forest of administrative division scale and pixel scale is comprehensively evaluated.The application proposes to carry out comprehensive evaluation on landscape features based on the size year differentiation degree of Phyllostachys edulis forest, compared with the prior art, the application creates new type Phyllostachys edulis forest size year differentiation index, enriches landscape analysis method, can multi-angle evaluation landscape feature, provides reference for local Phyllostachys edulis forest in size year management.

Description

Technical Field

[0001] This invention relates to the field of forestry remote sensing technology, and more specifically, to a method for evaluating the landscape differentiation characteristics of moso bamboo forests based on alternate bearing. Background Technology

[0002] bamboo( Phyllostachys edulis Moso bamboo is an important forest resource in China, accounting for approximately 70% of the country's bamboo forest area. Moso bamboo grows quickly, matures early, has high yields, and is widely used, possessing extremely high economic value. In addition, moso bamboo has a short harvesting cycle and a strong carbon sequestration capacity, playing an important role in addressing climate change. Moso bamboo forests exhibit a cyclical biennial phenomenon, characterized by abundant shoot production in bumper years and very little or no shoot production in the following year.

[0003] The study of alternate bearing in moso bamboo forests is a crucial aspect of moso bamboo forest ecosystem research. This phenomenon determines the growth range of bamboo shoots and the yield of bamboo, significantly impacting bamboo cultivation and management. As a unique phenomenon in moso bamboo forests, the spatial distribution information of alternate bearing is a vital foundation for research on moso bamboo forest ecosystems. Spatial differentiation, representing the unevenness and complexity of ecological variables in space, has a significant impact on tree growth. Surveys have revealed that moso bamboo exhibits significant spatial differentiation, and alternate bearing increases the complexity of this spatial differentiation, thus giving moso bamboo forests their own unique spatial distribution characteristics. However, traditional assessments of moso bamboo forest spatial distribution primarily focus on a comprehensive analysis of the distribution area and density of moso bamboo forests in a specific region. Furthermore, traditional assessments mainly explore the relationship between the degree of alternate bearing differentiation in moso bamboo forests and the regional ecosystem environment.

[0004] The impact of the biennial variation degree of bamboo forests on the ecological environment is mainly manifested by human or natural conditions. In order to explore the correlation between the biennial variation degree of bamboo forests and the degree of human and natural disturbances in the region, this invention proposes a landscape differentiation characteristic evaluation method based on the biennial variation degree of bamboo forests. On the basis of monitoring the biennial variation level of bamboo forests, a comprehensive evaluation of the landscape characteristics of bamboo forests in the region is carried out. Summary of the Invention

[0005] To address the shortcomings of existing technologies, the present invention aims to provide a method for evaluating the landscape differentiation characteristics of moso bamboo forests based on alternate bearing (biennial bearing). Remote sensing technology has the advantage of rapidly and accurately acquiring land cover distribution information, which is of great significance in moso bamboo forest monitoring. Therefore, this invention utilizes two years of Sentinel-2 remote sensing image data to extract the distribution information of alternate bearing in moso bamboo forests, providing a method for evaluating landscape differentiation characteristics based on the degree of differentiation between alternate bearing and landscape patterns. This method not only enriches the evaluation methods for moso bamboo forest landscape characteristics but also provides a reference for the management of local moso bamboo forests during alternate bearing periods.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for evaluating the landscape differentiation characteristics of moso bamboo forests based on alternate bearing, comprising the following steps:

[0007] Acquire regional boundary vector data and remote sensing image data;

[0008] Based on remote sensing image data, we obtained the classification data of alternate bearing and the distribution area of ​​alternate bearing in regional bamboo forests.

[0009] Based on the area of ​​alternate bearing in bamboo forests, we created the absolute alternate bearing differentiation index (AMODI), the relative alternate bearing differentiation index (OMODI), and the improved alternate bearing differentiation index (MMODI) for bamboo forests.

[0010] Based on the absolute biennial differentiation index (AMODI), the relative biennial differentiation index (OMODI), and the improved biennial differentiation index (MMODI), the biennial differentiation levels of bamboo forests are classified.

[0011] Based on the absolute biennial differentiation index (AMODI), the relative biennial differentiation index (OMODI), and the improved biennial differentiation index (MMODI), the landscape differentiation characteristics of bamboo forests are comprehensively evaluated.

[0012] According to the landscape differentiation characteristic evaluation method based on the biennial differentiation index of moso bamboo forests (absolute biennial differentiation index AMODI, relative biennial differentiation index OMODI, and improved biennial differentiation index MMODI) provided by the present invention, the method acquires two years of Sentinel-2 remote sensing image data, regional boundary vector data, and Quickbird high-resolution remote sensing image data provided by the European Space Agency (https: / / scihub.copernicus.eu).

[0013] Based on the acquired regional boundary vector data and remote sensing image data, the specific method and operation procedure for obtaining the alternating year classification data and the area of ​​alternating year distribution of bamboo forests in the region based on the remote sensing image data is as follows:

[0014] 1) Data preprocessing; Sentinel-2 two-year remote sensing image data was used to extract data for the classification of alternate bearing in moso bamboo forests. Preprocessing included radiometric calibration, atmospheric correction, resampling, mosaicking, and cropping. First, the Sentinel-2 two-year remote sensing image data was radiometrically calibrated and atmospherically corrected. The spatial resolution of the Sentinel-2 two-year remote sensing image data was uniformly resampled to 10 m using the cubic convolution method. The preprocessed data were then subjected to band fusion and image mosaicking, and cropped using regional boundary vector data.

[0015] 2) Extracting the area of ​​alternate bearing in bamboo forests; based on the preprocessed remote sensing image data, a hierarchical classification method is used, and non-bamboo forest land types are gradually eliminated based on decision trees to finally obtain the classification data of alternate bearing in regional bamboo forests; the main steps are as follows:

[0016] i) Differentiate evergreen vegetation from other land cover information based on the winter normalized vegetation index (NDVI); the formula for calculating the normalized vegetation index (NDVI) is:

[0017]

[0018] In the formula, The reflectivity is in the near-infrared band; The reflectivity is in the red light band;

[0019] ii) Based on evergreen vegetation information, the area of ​​alternating biennial distribution in bamboo forests is extracted according to the YCBI interannual index. The formula for calculating the YCBI interannual index of bamboo forests is as follows:

[0020]

[0021] In the formula, The reflectivity is in the near-infrared band; It is the red-edge band; m For months, y i For the year.

[0022] 3) Based on the hierarchical classification method, the classification threshold is set based on the decision tree, and non-bamboo forest land types are gradually eliminated. Finally, the alternate bearing of moso bamboo forests is classified, and the distribution area of ​​alternate bearing of moso bamboo forests is obtained respectively.

[0023] 4) Accuracy verification: Quickbird high-resolution remote sensing imagery was used to verify the accuracy of the classification results of alternate bearing of moso bamboo; using Sentinel-2 two-year remote sensing imagery data as a spatial reference, spatial registration was performed on the Quickbird high-resolution remote sensing imagery, with a registration accuracy within 1 pixel; the accuracy of the classification results of alternate bearing of moso bamboo was evaluated based on the confusion matrix.

[0024] According to the landscape differentiation characteristic evaluation method based on the biennial differentiation index (Absolute Biennial Differentiation Index AMODI, Relative Biennial Differentiation Index OMODI, and Improved Biennial Differentiation Index MMODI) of moso bamboo forests provided by the present invention, the biennial distribution area of ​​moso bamboo forests is obtained. By calculating and analyzing the biennial area, the Absolute Biennial Differentiation Index AMODI, Relative Biennial Differentiation Index OMODI, and Improved Biennial Differentiation Index MMODI of moso bamboo forests are created. The creation steps and calculation formulas are as follows:

[0025] (i)

[0026] In the formula, AMODI is the absolute biennial variation index of bamboo forests. A on-year For the area of ​​moso bamboo in a good year, A off-year The area of ​​moso bamboo during the small year;

[0027] (ii) Based on the coordinates of a point in a Cartesian coordinate system formed by the alternate annual area of ​​the bamboo forest, intersect the coordinates of this point with the straight line. y = x Perform deviation analysis on the points:

[0028]

[0029]

[0030]

[0031]

[0032]

[0033] In the formula The coordinates represent the actual area affected by both large and small years; The area of ​​moso bamboo in a good year; The area of ​​moso bamboo during the small year;

[0034] Let A be the foot of the perpendicular from point A to the baseline; The area of ​​bamboo planted in the year at point B. The area of ​​small bamboo at point B is [area not specified]. and They are equal; O is the origin of the coordinate system;

[0035] OMODI is the relative biennial variation index of moso bamboo forests;

[0036] (iii) Based on the absolute biennial differentiation index (AMODI) and the relative biennial differentiation index (OMODI) of bamboo forests, an improved biennial differentiation index (MMODI) of bamboo forests is constructed through mathematical analysis, and the calculation formula is as follows:

[0037]

[0038] In the formula, MMODI is the improved biennial variation index of moso bamboo forest, AMODI is the absolute biennial variation index of moso bamboo forest, and OMODI is the relative biennial variation index of moso bamboo forest.

[0039] 5) Based on the biennial variation results of bamboo forests, the variation level is monitored, that is, the variation level of bamboo forests in the region is monitored using remote sensing data of the bamboo forests to be monitored. The specific steps of variation level monitoring are as follows: (i) Obtain the processed remote sensing data of the monitoring area; (ii) Extract the remote sensing indices of key phenological periods of the bamboo forests to be monitored; (iii) Extract the area of ​​bamboo forests with biennial variation using the remote sensing indices; (iv) Perform deviation analysis on the area obtained in the previous step to create the absolute bamboo forest biennial variation index (AMODI), the relative bamboo forest biennial variation index (OMODI), and the improved bamboo forest biennial variation index (MMODI); (v) Calculate the degree of biennial variation of bamboo forests using the variation indices and formulate a classification table of classification standards to obtain the monitoring results of bamboo forest biennial variation level; the classification standards are as follows:

[0040] The degree of differentiation is extremely low: 0 ≤ AMODI (OMODI / MMODI) < 0.2;

[0041] The degree of differentiation is low: 0.2 ≤ AMODI (OMODI / MMODI) < 0.4;

[0042] The degree of differentiation is generally: 0.4 ≤ AMODI (OMODI / MMODI) < 0.6;

[0043] High degree of differentiation: 0.6 ≤ AMODI (OMODI / MMODI) < 0.8;

[0044] The degree of differentiation is extremely high: 0.8≤AMODI(OMODI / MMODI)≤1.

[0045] 6) According to the landscape differentiation characteristic evaluation method based on the alternate bearing differentiation index (absolute alternate bearing differentiation index AMODI, relative alternate bearing differentiation index OMODI, and improved alternate bearing differentiation index MMODI) of bamboo forests provided by the present invention, the degree of alternate bearing differentiation of bamboo forests is obtained by creating the absolute alternate bearing differentiation index AMODI, the relative alternate bearing differentiation index OMODI, and the improved alternate bearing differentiation index MMODI, and the differentiation level is monitored; the landscape differentiation characteristics of bamboo forests are comprehensively evaluated from different aspects; the landscape differentiation characteristics of bamboo forests are evaluated at the township level, and the spatial differentiation pattern of alternate bearing of bamboo forests is assessed.

[0046] Compared with existing technologies, this invention has the following advantages: it proposes a comprehensive evaluation of landscape characteristics based on the degree of biennial bearing differentiation in bamboo forests. Compared with existing evaluation methods, this invention creates the Absolute Biennial Bearing Differentiation Index (AMODI), the Relative Biennial Bearing Differentiation Index (OMODI), and the Improved Biennial Bearing Differentiation Index (MMODI) for bamboo forests, enriching landscape analysis methods and enabling multi-faceted evaluation of landscape characteristics, thus providing a reference for the management of local bamboo forests in terms of biennial bearing. Attached Figure Description

[0047] Figure 1 This is a flowchart illustrating the landscape differentiation characteristic evaluation method based on the alternate bearing differentiation index (absolute alternate bearing differentiation index AMODI, relative alternate bearing differentiation index OMODI, and improved alternate bearing differentiation index MMODI) of moso bamboo forests provided by the present invention.

[0048] Figure 2 This is a spatial distribution map of alternate bearing information of bamboo forests in Fuyang District, Hangzhou City, Zhejiang Province in 2018, based on the landscape differentiation characteristic evaluation method of bamboo forest alternate bearing differentiation index (absolute bamboo forest alternate bearing differentiation index AMODI, relative bamboo forest alternate bearing differentiation index OMODI and improved bamboo forest alternate bearing differentiation index MMODI) according to a specific implementation case of the present invention.

[0049] Figure 3 This is a schematic diagram illustrating the calculation method of the relative bamboo forest biennial differentiation index OMODI based on the biennial differentiation index of bamboo forests (absolute bamboo forest biennial differentiation index AMODI, relative bamboo forest biennial differentiation index OMODI, and improved bamboo forest biennial differentiation index MMODI) according to a specific implementation case of the present invention.

[0050] Figure 4This is a map showing the distribution of bamboo forest alternate bearing information in Fuyang District, Hangzhou City, Zhejiang Province in 2018 at the township-level administrative division, based on the landscape differentiation characteristic evaluation method of bamboo forest alternate bearing differentiation index (absolute bamboo forest alternate bearing differentiation index AMODI, relative bamboo forest alternate bearing differentiation index OMODI, and improved bamboo forest alternate bearing differentiation index MMODI).

[0051] Figure 5 Mapping of the spatial differentiation characteristics of alternate bearing in bamboo forests under the township administrative divisions of Fuyang District, Hangzhou City, Zhejiang Province in 2018, based on the Absolute Morphological Index (AMODI) of bamboo forests according to a specific implementation case of the present invention.

[0052] Figure 6 Mapping of the spatial differentiation characteristics of alternate bearing in bamboo forests under the township administrative divisions of Fuyang District, Hangzhou City, Zhejiang Province in 2018, based on the relative alternate bearing differentiation index (OMODI) of bamboo forests according to a specific implementation case of the present invention.

[0053] Figure 7 Mapping of the spatial differentiation characteristics of alternate bearing in bamboo forests under the township administrative divisions of Fuyang District, Hangzhou City, Zhejiang Province in 2018, based on the improved alternate bearing differentiation index MMODI of bamboo forests according to a specific implementation case of the present invention.

[0054] Figure 8 Mapping of the spatial differentiation characteristics of alternate bearing in bamboo forests at the pixel level (500 m) in Fuyang District, Hangzhou City, Zhejiang Province in 2018, based on the Absolute Morphological Index (AMODI) of bamboo forests according to a specific implementation case of the present invention.

[0055] Figure 9 Mapping of the spatial differentiation characteristics of alternate bearing in bamboo forests at the pixel level (500 m) in Fuyang District, Hangzhou City, Zhejiang Province in 2018, based on the relative alternate bearing differentiation index (OMODI) of bamboo forests according to a specific implementation case of the present invention.

[0056] Figure 10 Mapping of the spatial differentiation characteristics of alternate bearing in bamboo forests at the pixel level (500 m) in Fuyang District, Hangzhou City, Zhejiang Province in 2018, based on the improved alternate bearing differentiation index MMODI of bamboo forests according to a specific implementation case of the present invention. Detailed Implementation

[0057] Reference Figures 1-10 The present invention provides a further description of an embodiment of a method for evaluating the landscape differentiation characteristics of moso bamboo forests based on alternate bearing.

[0058] This case study focuses on Fuyang District, Hangzhou City, Zhejiang Province. It primarily utilizes Sentine-2 remote sensing imagery, regional boundary vector data, and Quickbird high-resolution remote sensing imagery. The various data products used in this paper are listed in the table below:

[0059] Table 1 Research Data Table

[0060]

[0061] Two years of Sentinel-2 remote sensing imagery data were used to extract the area of ​​alternate bearing in moso bamboo forests. The data was downloaded from the European Space Agency's data sharing website (https: / / scihub.copernicus.eu), with data dates of February 13, 2018, May 4, 2018, and May 24, 2019. Ten bands at 10 m and 20 m were selected, and the coordinate system was the global transverse Mercator 50-degree zone projection.

[0062] Quickbird high-resolution remote sensing imagery was used to verify the accuracy of alternating succession classification in moso bamboo forests; this data can be viewed in Google Earth. Administrative boundary vector data was used to crop the study area data and statistically analyze the spatial differentiation characteristics of alternating succession in moso bamboo forests under administrative divisions.

[0063] The main process of the landscape differentiation feature evaluation method based on the biennial differentiation index of moso bamboo forests (Absolute Biennial Differentiation Index AMODI, Relative Biennial Differentiation Index OMODI, and Improved Biennial Differentiation Index MMODI) in this case study includes: 1) Preprocessing the downloaded Sentinel-2 remote sensing image data from different times in 2018 and 2019 to obtain data for extracting the biennial area of ​​moso bamboo forests; 2) Using the Normalized Difference Vegetation Index (NDVI) in winter and the interannual biennial extraction index YCBI of moso bamboo forests, classification is performed based on decision trees. 1) Thresholds were set to extract data on alternate bearing in bamboo forests, and finally, data on the distribution of alternate bearing in bamboo forests were obtained; 2) Using QuickBird data in Google Earth, the accuracy of the obtained data on the distribution of alternate bearing in bamboo forests was verified by selecting land use samples from 2018; 3) By calculating the area of ​​alternate bearing, deviation analysis was performed to create the absolute alternate bearing differentiation index (AMODI), the relative alternate bearing differentiation index (OMODI), and the improved alternate bearing differentiation index (MMODI) of bamboo forests; 4) The differentiation index was used to calculate the degree of differentiation of alternate bearing in bamboo forests, and the results of the differentiation level monitoring were evaluated from two different levels: the township scale and the pixel scale (500 m).

[0064] This case study uses two years of Sentinel-2 remote sensing imagery to extract the area distribution of alternate bearing in bamboo forests. First, the two years of Sentinel-2 remote sensing imagery were preprocessed, including radiometric calibration, atmospheric correction, resampling, mosaicking, and cropping. SNAP-Sen2Cor software (http: / / step.esa.int / main / third-party-plugins-2 / sen2cor / ) was used to perform radiometric calibration and atmospheric correction on the Sentinel-2 Level-1C data to obtain the true reflectance of ground features, resulting in Level-2A data. Then, ENVI 5.3 software was used to resample the 20 m spatial resolution bands to 10 m using cubic convolution. Ten resampled bands were then fused and cropped using administrative boundary vector data to obtain the final remote sensing imagery used to extract the alternate bearing distribution of bamboo forests.

[0065] The acquisition of the area of ​​alternate bearing distribution in the bamboo forest of Fuyang District, Hangzhou City, Zhejiang Province in this case study includes the following steps:

[0066] 1) Using B4 (visible red band) and B8 (near-infrared band) from the Sentinel-2 two-year remote sensing imagery data dated February 13, 2018, NDVI = (B8-B4) / (B8+B4) was calculated using the Band Math tool in ENVI 5.3 software for winter imagery data of Fuyang District, Hangzhou City, Zhejiang Province. Decision tree classification was used, with NDVI = 0.5 as the threshold. Areas with NDVI greater than 0.5 were classified as vegetation, and non-vegetated land types such as urban areas were removed to obtain a vegetation information map. The formula for calculating NDVI is:

[0067]

[0068] In the formula, The reflectivity is in the near-infrared band; The reflectivity is in the red light band.

[0069] (2) Using B7 (vegetation red edge band), B8 (near-infrared band), and B8A (vegetation red edge band) from Sentinel-2 two-year remote sensing image data dated February 13, 2018, and May 24, 2019, YCBI = (B7 2019 +B8 2019 +B8A 2019 ) / (B7 2018 +B8 2018 +B8A 2018Using the Band Math tool in ENVI 5.3 software, the YCBI was calculated. Decision tree classification was used to select land types with a YCBI greater than 1.1 as low-yield bamboo forests, those with a YCBI less than 0.8 as high-yield bamboo forests, and those with a YCBI between 0.8 and 1.1 (inclusive) as other land types. Finally, the distribution information of high and low yields of bamboo forests in Fuyang District, Hangzhou City, Zhejiang Province was obtained (see attached instruction manual). Figure 3 The formula for calculating YCBI is as follows:

[0070]

[0071] In the formula, The reflectivity is in the near-infrared band; It is the red-edge band; m For months, y i For the year.

[0072] (3) Based on the real land cover data from the field survey and combined with QuickBird imagery data from Google Earth, 600 real land cover samples from 2018 were selected for accuracy verification. Producer accuracy (PA), user accuracy (UA), and overall accuracy (OA) of bamboo forests were calculated based on the confusion matrix. Among them, there were 150 bamboo forests in a bumper year and 150 bamboo forests in a lean year in 2018, and 300 other forests. The accuracy of extracting the biennial distribution information of bamboo forests was evaluated. The overall accuracy of the two regions was above 90%, indicating that the extraction effect was good. The accuracy evaluation results of this case are shown in the table below:

[0073] Table 2. Evaluation Table of Remote Sensing Classification Accuracy of Bamboo Forest

[0074]

[0075] (4) Based on the biennial distribution information of bamboo forests in Fuyang District, Hangzhou City, Zhejiang Province (see attached instructions). Figure 3 ), Analyze the area distribution of bamboo forests with alternating years and alternate bearing under township administrative divisions (see attached instruction manual). Figure 4 Based on the township-level administrative divisions of Fuyang District, Hangzhou City, Zhejiang Province, the highest proportion of bamboo harvests in large years is in Lingqiao Town at 12.10%, while the highest proportion of bamboo harvests in small years is in Changlu Town at 40.76%. The top three areas with the highest bamboo harvests in Fuyang District, Hangzhou City, Zhejiang Province are Dayuan Town (7.62 km²). 2 Xindeng Township 7.01 km 2 Yinhu Subdistrict 6.76 km 2 In 2018, the area of ​​bamboo in Fuyang District that was in a bumper year accounted for 31.48% of the total area of ​​bamboo in Fuyang District, and 1.18% of the total area of ​​Fuyang District. The top three areas in terms of bamboo area in a lean year were Wanshi Town (35.69 km²). 2 Xindeng Township 30.20 km2 Huyuan Township 27.86km 2 It accounts for 37.73% of the area of ​​moso bamboo in Fuyang District in 2018, and 5.18% of the total area of ​​Fuyang District.

[0076] This case study uses a landscape differentiation characteristic evaluation method based on the biennial variation index of moso bamboo forests (Absolute Moso Bamboo Forest Biennial Variation Index AMODI, Relative Moso Bamboo Forest Biennial Variation Index OMODI, and Improved Moso Bamboo Forest Biennial Variation Index MMODI). The method obtains the biennial variation distribution area of ​​moso bamboo forests, analyzes the calculated biennial variation area, and creates the Absolute Moso Bamboo Forest Biennial Variation Index AMODI, Relative Moso Bamboo Forest Biennial Variation Index OMODI, and Improved Moso Bamboo Forest Biennial Variation Index MMODI. The creation steps and calculation formulas are as follows:

[0077] (i)

[0078] In the formula, AMODI is the absolute biennial variation index of bamboo forests. A on-year For the area of ​​moso bamboo in a good year, A off-year The area of ​​moso bamboo during the small year;

[0079] (ii) Based on the coordinates of a point in a Cartesian coordinate system formed by the alternate annual area of ​​the bamboo forest, intersect the coordinates of this point with the straight line. y = x Perform deviation analysis on the points (see instruction manual). Figure 3 ):

[0080]

[0081]

[0082]

[0083]

[0084]

[0085] In the formula The coordinates represent the actual area affected by both large and small years; The area of ​​moso bamboo in a good year; The area of ​​moso bamboo during the small year;

[0086] Let A be the foot of the perpendicular from point A to the baseline; The area of ​​bamboo planted in the year at point B. The area of ​​small bamboo at point B is [area not specified]. and They are equal; O is the origin of the coordinate system;

[0087] OMODI is the relative biennial variation index of moso bamboo forests;

[0088] (iii) Based on the absolute biennial differentiation index (AMODI), the relative biennial differentiation index (OMODI), and the improved biennial differentiation index (MMODI) of bamboo forests, the calculation formula is as follows:

[0089]

[0090] In the formula, MMODI is the improved biennial variation index of moso bamboo forest, AMODI is the absolute biennial variation index of moso bamboo forest, and OMODI is the relative biennial variation index of moso bamboo forest.

[0091] This case study uses the differentiation index to calculate the degree of biennial variation in bamboo forests. It evaluates the monitoring results of biennial variation in bamboo forests from two aspects: the township administrative division scale and the pixel (500 m) scale. It also performs hierarchical mapping at the township scale and the pixel scale (500 m).

[0092] Results at the township level (see attached instruction manual) Figures 5-7 The bamboo forests in Yongchang Town, Fuyang District, Hangzhou City, Zhejiang Province, exhibited the highest degree of biennial variation, with absolute biennial variation index (AMODI), relative biennial variation index (OMODI), and improved biennial variation index (MMODI) of 0.903, 0.986, and 0.944, respectively. In contrast, the bamboo forests in Changlu Town exhibited the lowest degree of biennial variation, with absolute biennial variation index (AMODI), relative biennial variation index (OMODI), and improved biennial variation index (MMODI) of 0.012, 0.004, and 0.006, respectively.

[0093] Based on the results at the 500m pixel level (see attached manual) Figures 8-10 The degree of differentiation of moso bamboo forests is discretely distributed, with some pixels showing no biennial distribution of moso bamboo. In Fuyang District of Hangzhou City, Zhejiang Province, the degree of biennial differentiation of moso bamboo forests is low in most areas, with only a few areas showing a high degree of differentiation.

[0094] This case study employs a landscape differentiation characteristic evaluation method based on the biennial variation index of moso bamboo forests (Absolute Biennial Variation Index (AMODI), Relative Biennial Variation Index (OMODI), and Improved Biennial Variation Index (MMODI). Finally, it comprehensively evaluates the landscape differentiation characteristics of moso bamboo forests based on the spatial distribution and degree of biennial variation. At the township level, Xindeng Town in Fuyang District, Hangzhou City, Zhejiang Province, has a high distribution and proportion of moso bamboo, but its degree of biennial variation is very low. Conversely, Dongzhou Street, with a higher degree of differentiation, has a lower distribution area of ​​moso bamboo forests. At the 500-m pixel level, most areas in the district have a low degree of biennial variation, while areas with higher differentiation are scattered.

[0095] This case study details the evaluation method and process of landscape differentiation characteristics based on the biennial differentiation index of moso bamboo forests (Absolute Moso Bamboo Forest Biennial Differentiation Index AMODI, Relative Moso Bamboo Forest Biennial Differentiation Index OMODI, and Improved Moso Bamboo Forest Biennial Differentiation Index MMODI). Taking Fuyang District of Hangzhou City, Zhejiang Province as the study area, remote sensing image data from February 13, 2018, May 4, 2018, and May 24, 2019 were collected to obtain the classification data of biennial and biennial distribution of moso bamboo forests in the region. By calculating the area of ​​biennial and biennial distribution of moso bamboo forests and performing deviation analysis, new biennial and biennial differentiation indices (Absolute Moso Bamboo Forest Biennial Differentiation Index AMODI, Relative Moso Bamboo Forest Biennial Differentiation Index OMODI, and Improved Moso Bamboo Forest Biennial Differentiation Index MMODI) were created to monitor the biennial and biennial differentiation levels of moso bamboo forests and comprehensively evaluate the landscape differentiation characteristics of moso bamboo forests from multiple perspectives. The landscape differentiation characteristic evaluation method based on the alternate bearing differentiation index of moso bamboo forests (absolute alternate bearing differentiation index AMODI, relative alternate bearing differentiation index OMODI, and improved alternate bearing differentiation index MMODI) innovatively monitors the degree of differentiation of moso bamboo forests at different levels, enriches the evaluation method of moso bamboo forest landscape characteristics, is simple and easy to operate, has certain business application prospects, and can provide a reference for the management of local moso bamboo forests in terms of alternate bearing.

[0096] The above description, combined with specific case implementations, provides a further detailed explanation of the present invention, but it should not be construed as limiting the specific implementation of the present invention to these descriptions. Those skilled in the art can apply this invention and make some modifications, but these modifications or substitutions cannot depart from the conceptual framework of the present invention and should all be considered within the scope of protection of the present invention.

Claims

1. A method for evaluating the differentiation characteristics of a Phyllostachys edulis forest landscape based on the size of the years, characterized in that, It comprises the following steps: Obtain regional boundary vector data and remote sensing image data; Obtain regional bamboo forest size-year classification data and size-year distribution area based on remote sensing image data; Based on the size-year distribution area of bamboo forest, create absolute bamboo forest size-year differentiation index AMODI and relative bamboo forest size-year differentiation index OMODI; Based on the absolute bamboo forest size-year differentiation index AMODI and the relative bamboo forest size-year differentiation index OMODI, create the improved bamboo forest size-year differentiation index MMODI; Based on the absolute bamboo forest size-year differentiation index AMODI, the relative bamboo forest size-year differentiation index OMODI and the improved bamboo forest size-year differentiation index MMODI, divide the bamboo forest size-year differentiation level; Based on the absolute bamboo forest size-year differentiation index AMODI, the relative bamboo forest size-year differentiation index OMODI and the improved bamboo forest size-year differentiation index MMODI, comprehensively evaluate the bamboo forest size-year landscape differentiation characteristics of administrative division scale and pixel scale; Create the absolute bamboo forest size-year differentiation index AMODI by calculating the sum and difference value of bamboo forest size-year area and conducting deviation analysis, and the calculation formula is as follows: In the formula, AMODI is the absolute bamboo forest size annual differentiation index, A on-year is the large-year bamboo area, A off-year is the small-year bamboo area; According to the point coordinates of the size year area composition of the bamboo forest in the plane rectangular coordinate system, the point coordinates are analyzed for deviation from the point on the straight line y = x The absolute bamboo forest size year differentiation index OMODI is created, and the calculation formula is as follows: In the formula is the coordinate of the actual size year area; is the large year moso bamboo area; is the small year moso bamboo area; is the foot of the perpendicular from point A on the reference line; is the area of the Phyllostachys edulis in the B point in the big year, is the area of the Phyllostachys edulis in the B point in the small year, and are equal; O is the origin of the coordinate system; OMODI is the relative bamboo forest size-year differentiation index; According to the absolute bamboo forest size-year differentiation index AMODI and the relative bamboo forest size-year differentiation index OMODI, through mathematical analysis, the improved bamboo forest size-year differentiation index MMODI is constructed, and the calculation formula is as follows: In the formula, MMODI is the improved bamboo forest size-year differentiation index, AMODI is the absolute bamboo forest size-year differentiation index, and OMODI is the relative bamboo forest size-year differentiation index.

2. The method according to claim 1, wherein, Obtain Sentinel-2 two-year remote sensing image data, administrative boundary vector data and Quickbird high-resolution remote sensing image.

3. The method according to claim 2, wherein, The steps of obtaining regional bamboo forest size-year classification data and size-year distribution area based on remote sensing image data are as follows: (1) Data preprocessing; select Sentinel-2 two-year remote sensing image data for bamboo forest size-year classification data extraction, and the preprocessing includes radiation calibration, atmospheric correction, resampling, mosaicking and cropping; first, perform radiation calibration and atmospheric correction on the Sentinel-2 two-year remote sensing image data; use the three convolution method to uniformly resample the spatial resolution of the Sentinel-2 two-year remote sensing image data to 10 m; Fuse the above-mentioned pre-processed research data and image mosaic, and crop using the administrative vector boundary; (2) Extract the bamboo forest size-year area; based on the pre-processed remote sensing image data, use the hierarchical classification method, and based on the decision tree, gradually remove non-bamboo forest classes, and finally obtain the regional bamboo forest size-year classification data as follows: 1) Based on the winter normalized vegetation index NDVI, distinguish evergreen vegetation and other ground class information; the normalized vegetation index NDVI calculation formula is: In the formula, is the reflectivity in the near-infrared band; is the reflectivity in the red light band; 2) Based on the evergreen vegetation information, according to the bamboo forest size-year interannual extraction index YCBI, extract the bamboo forest size-year distribution area; the bamboo forest size-year interannual extraction index YCBI calculation formula is: In the formula, is the reflectivity in the near-infrared band; is the red edge band; m is the month, y i is the year; 3) According to the hierarchical classification method, the non-bamboo forest types are gradually removed based on the decision tree, and the distribution areas of the large and small year Phyllostachys edulis forests are finally obtained; 4) Precision verification: Quickbird high-resolution remote sensing images are selected to obtain verification samples for precision verification of the large and small year Phyllostachys edulis forest classification results; Sentinel-2 two-year remote sensing image data are used as spatial reference to spatially register the Quickbird high-resolution remote sensing images with a registration accuracy within 1 pixel; the accuracy of the large and small year Phyllostachys edulis forest classification results is evaluated based on the confusion matrix.

4. The method according to claim 3, wherein, According to the differentiation results, the differentiation grades of the large and small year Phyllostachys edulis forests are monitored; the specific steps of the differentiation grade monitoring are as follows: (i) obtaining the processed remote sensing data of the monitoring area; (ii) extracting the remote sensing indices of the key phenological periods of the Phyllostachys edulis forests to be monitored; (iii) using the remote sensing indices to extract the areas of the large and small year Phyllostachys edulis forests; (iv) performing dispersion analysis on the areas obtained in the previous step to create the absolute Phyllostachys edulis forest size year differentiation index AMODI, the relative Phyllostachys edulis forest size year differentiation index OMODI, and the improved Phyllostachys edulis forest size year differentiation index MMODI; (v) calculating the differentiation degree of the large and small year Phyllostachys edulis forests using the differentiation indices, and formulating a grade standard division table to obtain the monitoring results of the differentiation grades of the large and small year Phyllostachys edulis forests; the differentiation grade standard division is as follows: Very low differentiation degree: 0≤AMODI(OMODI / MMODI)<0.2; Low differentiation degree: 0.2≤AMODI(OMODI / MMODI)<0.4; Moderate differentiation degree: 0.4≤AMODI(OMODI / MMODI)<0.6; High differentiation degree: 0.6≤AMODI(OMODI / MMODI)<0.8; Very high differentiation degree: 0.8≤AMODI(OMODI / MMODI)≤1.

5. The method according to claim 1, wherein, The absolute Phyllostachys edulis forest size year differentiation index AMODI, the relative Phyllostachys edulis forest size year differentiation index OMODI, and the improved Phyllostachys edulis forest size year differentiation index MMODI are created to obtain the differentiation degree of the large and small year Phyllostachys edulis forests, and the differentiation grade monitoring is performed; the differentiation characteristics of the Phyllostachys edulis forest landscape are comprehensively evaluated from different aspects; the differentiation characteristics of the Phyllostachys edulis forest landscape are evaluated from the township scale level to assess the spatial differentiation pattern of the large and small year Phyllostachys edulis forests.

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