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CN-121982520-A - Damaged land identification method

CN121982520ACN 121982520 ACN121982520 ACN 121982520ACN-121982520-A

Abstract

The application relates to the technical field of environmental monitoring, and provides a damaged land identification method. And generating a vegetation index curve covering the annual time sequence based on the long-time-sequence remote sensing image data, wherein the vegetation index curve is used for representing the annual vegetation growth climate characteristics in the current annual earth surface latest state. And traversing and analyzing the vegetation index curve to respectively determine an annual maximum value index and an annual minimum value index, wherein the period corresponding to the annual maximum value index represents a vegetation growing season, and the period corresponding to the annual minimum value index represents a vegetation non-growing season. And determining an annual amplitude vegetation index according to the annual maximum value index and the annual minimum value index, wherein the annual amplitude vegetation index is used for quantifying the growth fluctuation amplitude of the current annual surface vegetation in the latest state. Detecting the annual change track, correcting the abnormal value of the annual change track, and identifying the damaged land according to the corrected change track.

Inventors

  • Huo Jiangrun
  • LI JING
  • YU HAIXIA

Assignees

  • 中国矿业大学(北京)

Dates

Publication Date
20260505
Application Date
20251231

Claims (10)

  1. 1. A method of identifying damaged land, the method comprising: acquiring long-time-sequence remote sensing image data according to the space range and the monitoring period of the area to be investigated, wherein the long-time-sequence remote sensing image data comprises blue light, green light, red light, near infrared and short wave infrared wave bands; generating a vegetation index curve covering an annual time sequence based on the long-time-sequence remote sensing image data, wherein the vegetation index curve is used for representing the annual vegetation growth weather characteristics in the current annual earth surface latest state; performing traversal analysis on the vegetation index curve to respectively determine an annual maximum value index and an annual minimum value index, wherein the period corresponding to the annual maximum value index represents a vegetation growing season, and the period corresponding to the annual minimum value index represents a vegetation non-growing season; Determining an annual amplitude vegetation index (IAAVI) according to the annual maximum value index and the annual minimum value index, wherein the annual amplitude vegetation index is the relative quantity formed by the annual maximum value index and the annual minimum value index difference and is used for quantifying the growth fluctuation amplitude of the current annual surface vegetation in the latest state; Detecting an annual IAAVI change track, and identifying and correcting an abnormal value in the annual IAAVI change track, wherein the annual IAAVI change track is a IAAVI change track comprising a plurality of years; and identifying damaged land according to the corrected annual IAAVI change track.
  2. 2. The method of claim 1, wherein the generating a vegetation index curve covering an annual time sequence based on the long-time-series remote sensing image data comprises: Selecting a vegetation index type, and performing wave band operation image by image based on the long-time sequence remote sensing image data to obtain a vegetation index time sequence of the vegetation index type, wherein the vegetation index is used for indicating the surface coverage type and the change characteristics of vegetation; fitting the vegetation index time sequence of each pixel to obtain a time sequence change track of the corresponding pixel; Performing breakpoint detection according to the time sequence change track, and determining an annual fitting coefficient according to a breakpoint detection result; And generating a vegetation index curve covering the annual time sequence according to the time sequence change track and the annual fitting coefficient, wherein the vegetation index curve is used for representing the annual vegetation growth climate characteristics in the current annual earth surface latest state.
  3. 3. The method of claim 2, wherein performing the band-wise operation based on the long-term remote sensing image data to obtain the vegetation index value comprises: Selecting a vegetation index type according to the type, stage and surface characteristics of the land damage-reclamation of the area to be investigated, wherein the vegetation index type comprises a normalized vegetation index, an enhanced vegetation index, a comprehensive forest characteristic index and/or a normalized degradation fraction index; And carrying out wave band operation on the long-time sequence remote sensing image data image by image to determine a vegetation index value corresponding to the vegetation index type.
  4. 4. The method according to claim 2, wherein the detecting the break point according to the time sequence variation track, and determining the annual fit coefficient according to the break point detection result, includes: Identifying a breakpoint in the time sequence variation track, wherein the breakpoint is used for indicating the transition of the pixel from one state to another state in a time sequence; Extracting the occurrence time corresponding to the breakpoint, the start-stop time of the segmentation before and after the breakpoint and the fitting coefficient; the method comprises the steps of taking the past year as a time unit, taking the annual fitting coefficient from the unique segmented fitting coefficient corresponding to the year when no break point exists in the year, updating the annual fitting coefficient to the fitting coefficient of the last segment when the break point exists in the year, and representing the latest state of the earth surface after the mutation event at the break point occurs.
  5. 5. The method of any one of claims 1-4, wherein the vegetation index curve includes a breakpoint therein, wherein the traversing analysis of the vegetation index curve determines an annual maximum indicator and an annual minimum indicator, respectively, and wherein the method further comprises modifying an annual maximum indicator of a representative remote sensing index based on an updated annual fit coefficient, wherein the annual maximum indicator includes the annual maximum indicator and the annual minimum indicator.
  6. 6. The method of any one of claims 1-4, wherein the identifying damaged land from the annual amplitude vegetation index change trajectory comprises: Detecting the annual amplitude vegetation index change track, and identifying a 'false breakpoint', wherein the 'false breakpoint' is an abnormal value unrelated to the real ground surface change; Correcting the abnormal value to obtain a corrected annual IAAVI change track; and analyzing each pixel in the area to be investigated one by one according to the corrected annual IAAVI change track, and identifying the damaged land.
  7. 7. The method of any one of claims 1-4, wherein the identifying damaged land from the annual amplitude vegetation index change trajectory comprises: Taking IAAVI change tracks of all pixels in the region to be investigated in a monitoring period as input samples, adopting an Otsu automatic threshold method, taking maximized inter-class variance as a principle, and determining a damage judgment threshold value through automatically searching an optimal partition point, wherein the damage judgment threshold value is used for judging the ground surface damage states of all the pixels in different years; and judging whether damage occurs or not according to the damage judging threshold value and IAAVI change tracks, and determining the damage occurrence year.
  8. 8. The method according to any one of claims 1-4, further comprising: Continuing annual IAAVI change track analysis on the damaged pixels in the area to be investigated so as to judge whether the damaged pixels are reclaimed and determine the reclamation implementation year of the damaged pixels; in the case where the determination result is that reclamation is performed, the state after reclamation is evaluated to determine whether reclamation is sufficient; and generating a damaged land space distribution map according to the land damage and reclamation states of the pixels.
  9. 9. The method according to any one of claims 1-4, further comprising: according to the harmonic fitting track of the remote sensing index and the annual IAAVI changing track, combining the damage and reclamation conditions and reclamation sufficiency, the land in the area to be investigated is classified into the following types, namely, the land which is not damaged, the land which is not reclaimed after damage, the insufficiently reclaimed land after damage and the fully reclaimed land after damage.
  10. 10. A computing device comprising at least one memory for storing a program, at least one processor for executing the program stored in the memory, the processor for performing the method of any one of claims 1-9 when the program stored in the memory is executed.

Description

Damaged land identification method Technical Field The application relates to the technical field of environmental monitoring, in particular to a damaged land identification method. Background In recent years, with the continuous acceleration of economic construction and resource development activities, the problems of land resource protection and management are increasingly highlighted. Land is not only a basic carrier for agricultural production, but also an important component of the ecological environment system. The method has important significance for scientifically making a land resource protection policy, implementing ecological correction engineering and promoting sustainable utilization. The area of the mining spoil land in 2019 nationally reached 361.05 ten thousand hm2, wherein the mine spoil land in construction was about 134.04 ten thousand hm2 and the mine spoil land in waste was about 227.01 ten thousand hm2. Currently, the problem that the damaged land in China is not clear at family property and has an unknown base is still commonly existed, the damaged land left in history is not recovered, the newly added damaged land is continuously increased, the main problem faced by the land reclamation work for a long time is that an efficient and accurate damaged land identification technology is needed to be established so as to realize rapid monitoring and dynamic updating of the damaged range and time sequence change. Disclosure of Invention The application provides a damaged land identification method, which comprises the steps of obtaining long-time-sequence remote sensing image data according to the space range and monitoring time period of an area to be investigated, wherein the long-time-sequence remote sensing image data comprise blue light, green light, red light, near infrared and short wave infrared wave bands, generating a vegetation index curve covering an annual time sequence based on the long-time-sequence remote sensing image data, wherein the vegetation index curve is used for representing the growth characteristics of annual vegetation in the current annual surface latest state, carrying out traversal analysis on the vegetation index curve, respectively determining an annual maximum value index and an annual minimum value index, wherein the corresponding time period of the annual maximum value index represents a vegetation growth season, the corresponding time period of the annual minimum value index represents a vegetation non-growth season, determining an annual amplitude vegetation index (Intra-Annual Amplitude Vegetation Index, IAAVI) according to the annual maximum value index and the annual minimum value index, wherein the annual amplitude vegetation index is the relative quantity formed by the annual maximum value index and the annual minimum value index, and is used for quantifying the relative quantity formed by difference value, carrying out traversal analysis on the vegetation index curve, respectively determining the annual maximum value index and the annual minimum value index is IAAVI, and the trace is identified to change according to the variation of the annual trace of the abnormal trace and IAAVI. In some possible embodiments, the generating a vegetation index curve covering the annual time sequence based on the long time sequence remote sensing image data includes selecting a vegetation index type, performing band operation on an image-by-image basis based on the long time sequence remote sensing image data to obtain a vegetation index time sequence of the vegetation index type, wherein the vegetation index is used for indicating a surface coverage type and a change characteristic of vegetation, fitting the vegetation index time sequence of each pixel to obtain a time sequence change track of the corresponding pixel, performing breakpoint detection according to the time sequence change track, determining an annual fit coefficient according to a breakpoint detection result, and generating a vegetation index curve covering the annual time sequence according to the time sequence change track and the annual fit coefficient, wherein the vegetation index curve is used for representing an annual vegetation growth weather characteristic in a current annual surface latest state. In some possible embodiments, the performing band operation on the long-time-sequence remote sensing image data image by image to obtain a vegetation index value includes selecting a vegetation index type by integrating the type, stage and surface features of the land damage-reclamation of the area to be investigated, wherein the vegetation index type includes a normalized vegetation index, an enhanced vegetation index, a comprehensive forest feature index and/or a normalized degradation fraction index, performing band operation on the long-time-sequence remote sensing image data image by image, and determining a vegetation index value corresponding to the vegetation index type. In some possibl