CN-122024046-A - Landscape scale grassland type division method using high spatial resolution image

CN122024046ACN 122024046 ACN122024046 ACN 122024046ACN-122024046-A

Abstract

The invention relates to the technical field of image or video recognition or understanding, and discloses a method for dividing the type of grassland in a landscape scale by utilizing high-spatial resolution images, wherein the grassland is divided step by comprehensively using a plurality of registered remote sensing images, and the dividing result of each stage ensures that pixels which are identical in appearance and represent different grasslands in the next stage are not positioned in the same partition, so that the accuracy of the dividing result of each stage is ensured, the regions which are mutually interweaved and distributed in a small range in various grasslands in different types can be accurately divided, and the division of the landscape scale can be realized after the multi-stage combination; meanwhile, as the data used in each stage is not coupled with the previous stage, the required acquisition processes of the single-day VHSR image, the HSR image time sequence data and the DEM image are not bundled, the data sources are not required, and the parameters are not required to be adjusted by manpower like deep learning. Thereby significantly reducing data acquisition costs and labor costs.

Inventors

JIA ZHIQING
HAN DONG
Fairy He Ling
WANG DEFU
WU RINA
WANG LONG
LI LIANXING

Assignees

中国林业科学研究院林业研究所

Dates

Publication Date: 20260512
Application Date: 20260121

Claims (7)

1. A method for dividing the types of landscaping grasslands by utilizing high-spatial resolution images is used for dividing the types of the landscaping grasslands in the regions which are mutually interweaved and distributed in a small range by a plurality of different grasslands, and is characterized by comprising the following steps: firstly, a grassland to be divided is marked as a grassland to be divided, DEM images of the grassland to be divided, single-day VHSR images in summer and HSR images covering the whole vegetative growth period are collected and registered, so that pixels corresponding to the three images represent the same position on the ground; dividing the single-day VHSR image into a plurality of different homogeneous areas and marking the homogeneous areas as first-level partitions; Counting the change rule of the grassland medium in each first-level partition based on the HSR image time sequence data; Establishing a database of the change rule of the artificial intervention grassland and the purely natural grassland medium based on the local climate and the production condition of agriculture and animal husbandry, subdividing each primary subarea again based on the change rule of the grassland medium in each primary subarea, and splitting two secondary subareas of the artificial area and the natural area from each primary subarea; Extracting the opening degree of a medium based on texture data in a single-day VHSR image, subdividing each secondary partition based on the opening degree of the medium, dividing the grassland type based on the function, and marking the grassland type as a tertiary partition; and step six, investigating the influence of the elevation and the gradient in the grassland to be divided on the grassland management mode, then subdividing each three-level partition again based on the DEM image, dividing the grassland type based on the grassland management mode, and recording as four-level partition.
2. The method for classifying landscaping scale grassland types by using high-spatial resolution images according to claim 1, wherein in the first step, registration is performed by adopting a characteristic point matching mode, and all characteristic points on steep slopes are removed when the registration is performed; In the registered images, no matter whether VHSR images and HSR images are subjected to orthographic correction or not, the orthographic correction needs to be performed on VHSR images and HSR images by using DEM images participated in the division at this time.
3. The method for classifying landscaping scale grassland types by using high-spatial resolution images according to claim 1, wherein the method comprises the following steps: step 4.1, obtaining a function of the integral average NDVI value of a known natural area in the grassland to be divided along with time, and recording the function as a natural NDVI function; step 4.2, obtaining a natural NDVI change function by solving a first order difference of the natural NDVI function, extracting peak points and valley points from the natural NDVI change function, and recording the peak points and the valley points as natural coverage season jump points; Analyzing first order difference of functions of integral average NDVI values of the HSR image pixels in each first-level subarea along with time change, extracting peak points and valley points in the first-level subarea, and recording the peak points and the valley points as actual coverage jump points; And 4.4, removing outliers in the natural pixels, wherein the region formed by the residual natural pixels is a natural region, and the region outside the natural region is an artificial region.
4. The method for classifying landscaping scale grassland types by using high-spatial resolution images according to claim 1, wherein the method comprises the following steps: Step 6.1, investigating the influence of the elevation and the gradient in the grassland to be divided on the grassland management mode, and solving an elevation difference threshold M which causes the change of the grassland management mode; Step 6.2, finding out an easily-identified grassland management mode dividing line in the grasslands to be divided, and recording the elevation of the dividing line; and 6.3, using the DEM image as a regional landscape matrix to define contour lines with the M equal-altitude distance as boundaries of different grassland management modes.
5. The method of claim 1, wherein in the fifth step, haralick index and Structure Feature Set (SFS) index are extracted based on VHSR images, and then classification is supervised by a support vector machine.
6. The method for classifying landscaping scale grassland types using high spatial resolution images according to claim 5, wherein: in the fifth step, the natural area is divided into three-level subareas of pasture, mowing and grazing dual-purpose pasture and difficult-to-use pasture, and the artificial area is divided into three-level subareas of permanent mowing, permanent grazing and short-term rotation pasture. In step six, the four-level plot includes permanent grass, short-term crop-rotation grass, and annual grass.
7. The method for classifying landscaping scale grassland types by using high-spatial resolution images according to claim 1, wherein in the second step, a mean shift algorithm is adopted for segmentation.

Description

Landscape scale grassland type division method using high spatial resolution image Technical Field The invention relates to the technical field of image or video recognition or understanding, in particular to a landscape scale grassland type dividing method utilizing high-spatial resolution images. Background Grasslands are one of the most widely distributed land use types worldwide. They cover approximately 25% of the land area and are an important component of the land ecosystem. China is one of the most abundant countries in the world, the total area of grasslands is about 4 hundred million hectares, and the grassland vegetation occupies about 27% of the total area of the whole country, and the grassland vegetation dominates the green ecological barrier of China. However, grasslands worldwide have degenerated due to the effects of climate change and human activity, and natural disasters such as water storage drop, water and soil loss, sand storm and the like are caused, directly affecting the sustainable development of society and economy. Thus, grass degradation monitoring is particularly important. The basis and premise of the grassland degradation monitoring is the monitoring of the grassland type, and the monitoring content should include the reduction of the grassland coverage, the change of the grassland utilization type and the change of the vegetation species composition. Therefore, the classification of grassland types is an important link between the monitoring of the grassland degradation process and the management of grassland ecological restoration. Traditional grassland type classification mainly relies on manual field work, and the classification result mainly depends on the expertise level and practical experience of a monitor. The method has low precision, and is difficult to obtain real-time and large-scale regional data due to various grassland types and extremely wide range. In addition, the method consumes a great deal of manpower and material resources and restricts the effective propulsion of ecological restoration of grasslands. With the development of remote sensing technology, the method is widely applied to the fields of earth resources, environmental science, disaster monitoring and the like, and the large-space-scale remote sensing data make up the limitation of the traditional ground measurement method in space. The grassland monitoring is gradually perfected, and the trend of higher efficiency, higher precision and larger spatial scale is presented. High spatial resolution images are the main data source in the current remote sensing technology field, and contain abundant spatial, radiation and spectral information. However, due to the high similarity between grassland types, the same type has different spectrums, and the different types have the same spectrums, which is very common, and brings difficulty to the classification of grassland types, so that the recognition result is quite inaccurate and can only be recognized in a fuzzy way in a large range, and the grassland type classification of the so-called 'landscape scale' with the most guiding significance for production is the scale of tens of hundreds of meters, the image of VHSR (very high spatial resolution) needed by the classification mode can be acquired, and the analysis means of the classification mode can be developed for many years, so that the relatively fine plane structure can be processed. However, this is still insufficient, and at present, such a processing method cannot process plots with complex terrain and various grass canine teeth staggered, and a large number of samples are needed for training (deep learning), so that more manpower and material resources are needed for parameter tuning. However, the grasslands in China have complex terrains and the canine teeth of various grasslands are staggered. The agriculture with very high development level is achieved thousands of years ago in China, and a large number of people are in the population since ancient times, so that the land development and utilization are extremely achieved. Historically, high quality grasslands with good hydrothermal conditions have been mostly converted into farmlands, even with many hills developed as terraces, and existing grasslands are located in ecologically marginal areas. The grass left as pasture is either broken up to be unusable or insufficient precipitation to support conventional crop growth. For example, even the inner mongolia, which has a large amount of grassland in public awareness, is not continuous with one type of grassland, but has a high degree of heterogeneity and diversity. The variation in hydrothermal conditions and human activity can cause a piece of grass to become a variety of different types. This greatly increases the difficulty of dividing the grassland types. Disclosure of Invention The invention provides a method for dividing the types of landscaping scale grasslands by utilizing high-spatia