NL2031646A - Method and device for determining vegetation restoration and construction regions of coastal beach wetlands - Google Patents

Method and device for determining vegetation restoration and construction regions of coastal beach wetlands Download PDF

Info

Publication number
NL2031646A
NL2031646A NL2031646A NL2031646A NL2031646A NL 2031646 A NL2031646 A NL 2031646A NL 2031646 A NL2031646 A NL 2031646A NL 2031646 A NL2031646 A NL 2031646A NL 2031646 A NL2031646 A NL 2031646A
Authority
NL
Netherlands
Prior art keywords
vegetation
bare
remote sensing
pioneer
mud
Prior art date
Application number
NL2031646A
Other languages
Dutch (nl)
Other versions
NL2031646B1 (en
Inventor
Zhang Yaqiong
Jia Xing
Meng Bin
Ma Wandong
Zhao Qian
Wang Nan
Zhao Huan
Wang Qingtao
Yin Shoujing
Gao Jixi
Zhang Jianhui
Yang Hongyan
Huang Li
Wang Xuelei
Wang Chen
Xu Dan
Zhu Nanhuanuowa
Original Assignee
Satellite Application Center For Ecology And Env Mee
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Satellite Application Center For Ecology And Env Mee filed Critical Satellite Application Center For Ecology And Env Mee
Publication of NL2031646A publication Critical patent/NL2031646A/en
Application granted granted Critical
Publication of NL2031646B1 publication Critical patent/NL2031646B1/en

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/11—Region-based segmentation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • G06T7/0014—Biomedical image inspection using an image reference approach
    • G06T7/0016—Biomedical image inspection using an image reference approach involving temporal comparison
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/80—Geometric correction
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/187—Segmentation; Edge detection involving region growing; involving region merging; involving connected component labelling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/60—Analysis of geometric attributes
    • G06T7/62—Analysis of geometric attributes of area, perimeter, diameter or volume
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/58—Extraction of image or video features relating to hyperspectral data
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/10—Terrestrial scenes
    • G06V20/13—Satellite images
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/10—Terrestrial scenes
    • G06V20/188—Vegetation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10016—Video; Image sequence
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10032—Satellite or aerial image; Remote sensing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20112—Image segmentation details
    • G06T2207/20132—Image cropping
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30181—Earth observation
    • G06T2207/30188—Vegetation; Agriculture

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Multimedia (AREA)
  • General Health & Medical Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Quality & Reliability (AREA)
  • Radiology & Medical Imaging (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Medical Informatics (AREA)
  • Geometry (AREA)
  • Astronomy & Astrophysics (AREA)
  • Remote Sensing (AREA)
  • Spectroscopy & Molecular Physics (AREA)
  • Image Processing (AREA)

Abstract

The present invention discloses a Hethod and device for determining vegetation restoration and construction regions of coastal beach wetlands, belonging to the field of vegetation restoration of coastal beach wetlands. In the present invention, a normalized. difference vegetation. index is calculated. based. on a remote sensing image, and. spatial distributions of vegetation, bare mudflat and pioneer vegetation are extracted. A probability of the pioneer vegetation appearing in each elevation interval on the bare mudflat is calculated. in combination. with the spatial distributions of the bare mudflat and the pioneer vegetation based on elevation data. The bare mudflat is subjected. to regional division according to the elevation intervals to obtain a spatial distribution. map of the probability of the pioneer vegetation appearing on the bare mudflat. Vegetation restoration and construction priority regions are determined according to the spatial distribution map of the probability of the pion

Description

METHOD AND DEVICE FOR DETERMINING VEGETATION RESTORATION AND CONSTRUCTION REGIONS OF COASTAL BEACH WETLANDS
TECHNICAL FIELD The present invention relates to the field of vegetation res- toration of coastal beach wetlands, in particular, to a method and device for determining vegetation restoration and construction re- gions of coastal beach wetlands.
BACKGROUND ART A coastal beach wetland is one of ecosystems with the highest productivity and biodiversity, which is located in the intersec- tion of marine and terrestrial ecosystems. In recent years, the coastal beach wetland has been seriously degraded under the influ- ence of human activities and global climate change, and therefore the protection and restoration of the coastal beach wetland has become a global environmental problem. Beach vegetation is an im- portant constituent part of the coastal beach wetland, which has important ecosystem service functions such as maintaining biodi- versity, purifying water pollution, storing and fixing carbon, re- ducing wind and waves, promoting siltation and land reclamation, and protecting beach and shore engineering facilities. However, different ecological functions are oriented to different regions, and the objectives and modes of restoration should be different. Therefore, scientifically and effectively determining restoration and construction regions of coastal beach wetland vegetation is of great significance for the protection and restoration of the coastal beach wetland.
There are many methods to restore the coastal beach wetland, but the restoration of beach vegetation is still weak. At present, methods for determining vegetation restoration and construction regions of coastal beach wetlands are mainly field investigation, data investigation and expert judgment. A restoration region range is selected by investigating a status before damage, damage rea- sons and the degree of human disturbance in a restoration process of a target coastal beach wetland.
At present, methods for determining vegetation restoration and construction regions of coastal beach wetlands are of a fully manual participation type, and are advantageous by being simple and feasible for small-area regions. The disadvantages are that a lot of manpower, material and financial resources are needed to rely solely on manual field investigation. In addition, it is dif- ficult to investigate large-area regions lack of data. Moreover, due to the unique location and environment of coastal beach wet- lands, natural conditions are harsh, and many regions are diffi- cult to reach. Meanwhile, many coastal beach wetlands have complex topography and hydrodynamic conditions and are lack of relevant data. The above-mentioned disadvantages may lead to a misjudgment when selecting a vegetation restoration and construction region, so that the selected vegetation restoration and construction re- gion is not necessarily suitable for vegetation growth. Finally the objective of vegetation restoration in the selected vegetation restoration and construction region is difficult to achieve, thus wasting the manpower and material resources of vegetation restora- tion projects.
SUMMARY In order to solve the above-mentioned technical problem, the present invention provides a method and device for determining vegetation restoration and construction regions of coastal beach wetlands. The present invention can objectively and comprehensive- ly obtain a large region suitable for vegetation restoration and construction of coastal beach wetlands, provide a target region for vegetation restoration of coastal wetlands, and improve the accuracy and effectiveness of coastal wetland vegetation restora- tion projects.
The technical solution provided by the present invention is as follows: A method for determining vegetation restoration and construc- tion regions of coastal beach wetlands includes: Sl: acquiring a remote sensing image and elevation data of a low-tide period of a research region;
S2: pre-processing the remote sensing image to obtain a re- mote sensing reflectance image, the pre-processing including geo- metric correction, spatial clipping, radiometric calibration, ap- parent reflectance calculation, and atmospheric correction;
S53: extracting a normalized difference vegetation index NDVI of the remote sensing reflectance image using a normalized differ- ence vegetation index method to obtain a normalized difference vegetation index distribution map, the normalized difference vege- tation index being obtained by the following formula:
NDVI = Pam Pied Pym Td Pied where NDVI is a normalized difference vegetation index, and Presa and Oms are remote sensing reflectance of a red band and a near-infrared band; S4: extracting vegetation patches and bare mudflat patches according to the normalized difference vegetation index distribu- tion map, and extracting pioneer vegetation patches scattered in the bare mudflat patches according to distributions of the vegeta- tion patches and the bare mudflat patches; S5: clipping the elevation data using the bare mudflat patch- es and the pioneer vegetation patches respectively to obtain bare mudflat elevation patches and pioneer vegetation elevation patch- es; S6: dividing the elevation data into a plurality of elevation intervals according to a certain height spacing, and calculating an area A; of the bare mudflat elevation patches and an area B; of the pioneer vegetation elevation patches in each elevation inter- val respectively, where i is a number of the elevation interval; S7: calculating a probability P; of pioneer vegetation ap- pearing in each elevation interval on a bare mudflat, P;i=A;/ ({A;+B;); S8: performing regional division on the bare mudflat patches according to the elevation intervals to obtain various bare mud- flat distribution intervals, and assigning the probability P; of the pioneer vegetation appearing in each elevation interval on the bare mudflat into the various bare mudflat distribution intervals to obtain a spatial distribution map of the probability of the pi-
oneer vegetation appearing on the bare mudflat; S9: determining vegetation restoration and construction re- gions and a priority level of each vegetation restoration and con- struction region according to the spatial distribution map of the probability of the pioneer vegetation appearing on the bare mud- flat.
Further, the radiometric calibration is performed by the fol- lowing formula: L=Gain*DN+Offset L is apparent radiance in Wm :sr umd; DN is a digital gray value of the remote sensing image; Gain is a gain of an absolute calibration coefficient in Wem“ srt um; Offset is an offset of the absolute calibration coefficient in Wm“ sr um™, and a vacancy value is 0. Further, the apparent reflectance calculation is performed by the following formula: TIry roa FoosE where pom is apparent reflectance at the top of atmosphere; D is a ratio of an actual Earth-Sun distance to a mean Earth- Sun distance; Fy is solar spectrum illuminance at the top cf atmosphere at the mean Earth-Sun distance in Wm“ um}; 6: is a solar zenith angle.
Further, after S2 and before 83, the method further includes: S21: acquiring a remote sensing reflectance image of a coastal beach wetland region from the remote sensing reflectance image.
Further, 321 includes: S211: performing water-land separation on the remote sensing reflectance image to obtain a remote sensing reflectance image of a preliminarily determined coastal beach wetland region; S212: performing a mask operation on an edge of the remote sensing reflectance image of the preliminarily determined coastal beach wetland region to obtain a remote sensing reflectance image of a coastal beach wetland region.
A device for determining vegetation restoration and construc- tion regions of coastal beach wetlands includes: a data acquisition module, configured to acquire a remote 5 sensing image and elevation data of a low-tide period of a re- search region; a pre-processing module, configured to pre-process the remote sensing image to obtain a remote sensing reflectance image, the pre-processing including geometric correction, spatial clipping, radiometric calibration, apparent reflectance calculation, and at- mospheric correction; a normalized difference vegetation index extraction module, configured to extract a normalized difference vegetation index NDVI of the remote sensing reflectance image using a normalized difference vegetation index method to obtain a normalized differ- ence vegetation index distribution map, the normalized difference vegetation index being obtained by the following formula: NDVI = Pam Pte Pum Pred , where NDVI is a normalized difference vegetation index, and Dg and pyr are remote sensing reflectance of a red band and a near-infrared band; a patch extraction module, configured to extract vegetation patches and bare mudflat patches according to the normalized dif- ference vegetation index distribution map, and extract pioneer vegetation patches scattered in the bare mudflat patches according to distributions of the vegetation patches and the bare mudflat patches; an elevation data clipping module, configured to clip the el- evation data using the bare mudflat patches and the pioneer vege- tation patches respectively to obtain bare mudflat elevation patches and pioneer vegetation elevation patches; an elevation interval division module, configured to divide the elevation data into a plurality of elevation intervals accord- ing to a certain height spacing, and calculate an area A; of the bare mudflat elevation patches and an area B; of the pioneer vege-
tation elevation patches in each elevation interval respectively, where 1 is a number of the elevation interval; a probability calculation module, configured to calculate a probability P; of pioneer vegetation appearing in each elevation interval on a bare mudflat, P,=A./(A;+B;):; a bare mudflat division and probability assignment module, configured to perform regional division on the bare mudflat patch- es according to the elevation intervals to obtain various bare mudflat distribution intervals, and assign the probability P; of the pioneer vegetation appearing in each elevation interval on the bare mudflat into the various bare mudflat distribution intervals to obtain a spatial distribution map of the probability of the pi- oneer vegetation appearing on the bare mudflat; a vegetation restoration and construction region determina- tion module, configured to determine vegetation restoration and construction regions and a priority level of each vegetation res- toration and construction region according to the spatial distri- bution map of the probability of the pioneer vegetation appearing on the bare mudflat.
Further, the radiometric calibration is performed by the fol- lowing formula: L=Gain*DN+Offset L is apparent radiance in Wm sr um’; DN is a digital gray value of the remote sensing image; Gain is a gain of an absolute calibration coefficient in Wem srt um; Offset is an offset of the absolute calibration coefficient in Wm” sr um, and a vacancy value is 0.
Further, the apparent reflectance calculation is performed by the following formula:
FID Toa Ff, cos 3 where Pros is apparent reflectance at the top of atmosphere; D is a ratio of an actual Earth-Sun distance to a mean Earth- sun distance; Fo is solar spectrum illuminance at the top of atmosphere at the mean Earth-Sun distance in Wm? um‘; 8, is a solar zenith angle. Further, the device further includes: a coastal beach wetland region acquisition module, configured to acquire a remote sensing reflectance image of a coastal beach wetland region from the remote sensing reflectance image. Further, the coastal beach wetland region acquisition module further includes: a water-land separation unit, configured to perform water- land separation on the remote sensing reflectance image to obtain a remote sensing reflectance image of a preliminarily determined coastal beach wetland region; an edge mask unit, configured to perform a mask operation on an edge of the remote sensing reflectance image of the preliminar- ily determined coastal beach wetland region to obtain a remote sensing reflectance image of a coastal beach wetland region. The present invention has the following beneficial effects: According to the present invention, the problem of difficulty in comprehensively investigating and accurately judging coastal wetlands with harsh natural conditions, poor accessibility and complex topography and hydrodynamic conditions according to tradi- tional on-site manual investigation can be solved, a large region suitable for vegetation restoration and construction of coastal wetland beaches can be objectively and comprehensively acquired, a target region for vegetation restoration of coastal wetlands can be provided, the accuracy and effectiveness of coastal wetland vegetation restoration projects can be improved, and technical support can be provided for coastal wetland restoration and pro- tection.
BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 is a flowchart of a method for determining vegetation restoration and construction regions of coastal beach wetlands ac- cording to the present invention. FIG. 2 is a schematic diagram of a device for determining vegetation restoration and construction regions of coastal beach wetlands according to the present invention.
FIG. 3 is a distribution example diagram of vegetation patch- es, bare mudflat patches and pioneer vegetation patches.
DETAILED DESCRIPTION OF THE EMBODIMENTS In order that the technical problems to be solved, technical solutions and advantages of the present invention will become more apparent, the present invention will be described in detail below with reference to the accompanying drawings and specific embodi- ments.
Embodiment 1: The embodiment of the present invention provides a method for determining vegetation restoration and construction regions of coastal beach wetlands. As shown in FIG. 1, the method includes the following steps: Sl: Acquire a remote sensing image and elevation data of a low-tide period of a research region.
In this step, the remote sensing image is a multi-spectral high-resolution remote sensing image. Preferably, satellite remote sensing image data having red (630-690 nm) and near-infrared (760- 900 nm) bands is selected. The spatial resolution of the remote sensing image should depend on the size of a coastal beach wetland region (i.e. research region). As the area of the coastal beach wetland region is smaller, spatial resolution requirements are higher. The remote sensing image data should cover at least the coastal beach wetland region. In the coastal beach wetland region, cloud cover is less than 50%.
High-accuracy digital elevation model (DEM) elevation data of a low-tide period of a research region may be acquired through airborne laser radar (Lidar) and other methods.
In order to ensure the integrity of the coastal beach wetland region, the acquired remote sensing image and elevation data are data of the low-tide period.
32: Pre-process the remote sensing image to obtain a remote sensing reflectance image, the pre-processing including geometric correction, spatial clipping, radiometric calibration, apparent reflectance calculation, and atmospheric correction.
Geometric correction: in remote sensing imaging, because of the influence of the attitude, altitude and speed of an aircraft and the rotation of the Earth, a geometric distortion of an image relative to a ground target occurs. The distortion shows that an actual position of a pixel relative to the ground target is squeezed, twisted, stretched, offset, etc. Error correction for geometric distortion is called the geometric correction.
During the geometric correction, image geometric accuracy correction and spatial projection conversion are performed based on reference images or spatial geometric information, and the ac- curacy 1s controlled within 1 pixel.
Spatial clipping: according to the longitude and latitude of an upper left corner and a lower right corner of a monitored lake and reservoir region, selected remote sensing images are spatially clipped, and the range is slightly larger than a lake and reser- voir water region.
Radiometric calibration: the radiometric calibration is per- formed according to a calibration formula of a remote sensor and calibration coefficients of each band, and the radiometric cali- bration formula is: L=Gain*DN+Offset where L is apparent radiance in Wm sr um}.
DN is a digital gray value of the remote sensing image; Gain is a gain of an absolute calibration coefficient in Wm srt am; Offset is an offset of the absolute calibration coefficient in Wm™ sr um, and a vacancy value is 0.
Apparent reflectance calculation: according to the apparent radiance of each band obtained through the radiometric calibra- tion, the apparent reflectance of each band is calculated accord- ing to the following formula:
FID Toa Ff, cos 3 where Pros is apparent reflectance (dimensionless) at the top of atmosphere; D is a ratio of an actual Earth-Sun distance to a mean Earth- Sun distance;
Fy is solar spectrum illuminance at the top of atmosphere at the mean Earth-Sun distance in Wm um; 6, is a solar zenith angle.
Atmospheric correction: atmospheric correction by satellite remote sensing in visible and near-infrared bands mainly focuses on the influence of atmospheric molecular scattering, aerosol scattering and water vapor absorption. The atmospheric correction may be performed by using methods based on radiative transfer mod- els (such as a 6S model and a Flaash atmospheric correction model) to obtain surface reflectance of each band, which is also called reflectance pga at the bottom of atmosphere.
S3: Extract a normalized difference vegetation index NDVI of the remote sensing reflectance image using a normalized difference vegetation index method to obtain a normalized difference vegeta- tion index distribution map, the normalized difference vegetation index being obtained by the following formula: NDVT = Pam Pea Pym Pred | where NDVI is a normalized difference vegetation index, and Pres and pyrg are remote sensing reflectance of a red band and a near-infrared band.
Due to the strong absorption of vegetation in the red band, the reflectance of the red band is low. Therefore, the remote sensing image has the characteristic of "reflection peak plateau effect” in the near-infrared band, and the reflectance of the near-infrared band is high. Non-vegetation (e.g. bare mudflats, etc.) have a lower reflectance in the near-infrared band. There- fore, by calculating a normalized difference vegetation index, vegetation and non-vegetation may be distinguished using the char- acteristic that a normalized difference vegetation index (NDVI) value of vegetation is higher than that of non-vegetation.
S4: Extract vegetation patches and bare mudflat patches ac- cording to the normalized vegetation index distribution map, and extract pioneer vegetation patches scattered in the bare mudflat patches according to distributions of the vegetation patches and the bare mudflat patches.
The bare mudflat refers to a flat with no vegetation at a wa- ter side. The pioneer vegetation is also called pioneer plants, referring to plants which appear first in community succession, that is, plants which appear first in the bare mudflat when vege- tation spreads to the bare mudflat.
Since the NDVI value of vegetation is higher than that of the bare mudflat, vegetation patches and bare mudflat patches may be extracted by setting a threshold. For example, if the NDVI value is higher than the threshold, it is vegetation, otherwise, it is bare mudflat. Illustratively, the threshold may be set to 0, pix- els with the NDVI value of more than 0 are vegetation, the vegeta- tion pixels are assigned with 1 for marking, and all the vegeta- tion pixels constitute a vegetation patch. Pixels with the NDVI value of less than 0 are bare mudflat, bare mudflat pixels are as- signed with 0 for marking, and all the bare mudflat pixels consti- tute a bare mudflat patch.
The pioneer vegetation is a plant that appears first in the bare mudflat when the vegetation spreads to the bare mudflat. Therefore, when determining pioneer vegetation patches, the pio- neer vegetation patches may be determined according to location distributions of the vegetation patches and the bare mudflat patches and the size of the vegetation patches. If the vegetation patches are small, and the vegetation patches are scattered in the bare mudflat patches and isolated from main vegetation patches connected to the land, the vegetation patches are pioneer vegeta- tion patches, as shown in FIG. 3.
S5: Clip the elevation data using the bare mudflat patches and the pioneer vegetation patches respectively to obtain bare mudflat elevation patches and pioneer vegetation elevation patch- es.
The bare mudflat elevation patches and pioneer vegetation el- evation patches refer to elevation data at the bare mudflat and elevation data at the pioneer vegetation, and reflect the distri- butions of the bare mudflat and the vegetation on the elevation data.
S6: Divide the elevation data into a plurality of elevation intervals according to a certain height spacing, and calculate an area A; of the bare mudflat elevation patches and an area B; of the pioneer vegetation elevation patches in each elevation interval respectively, where i is a number of the elevation interval.
Illustratively, the height spacing may be set to 10 cm or 20 cm, etc. and the area A; of the bare mudflat elevation patches and the area B; of the pioneer vegetation elevation patches in each elevation interval reflect the areas of the bare mudflat and the pioneer vegetation in each elevation interval i.
S7: Calculate a probability P; of pioneer vegetation appear- ing in each elevation interval on a bare mudflat, P;=A;/ (A;+B;).
S8: Perform regional division on the bare mudflat patches ac- cording to the elevation intervals to obtain various bare mudflat distribution intervals, and assign the probability P; of the pio- neer vegetation appearing in each elevation interval on the bare mudflat into the various bare mudflat distribution intervals to obtain a spatial distribution map of the probability of the pio- neer vegetation appearing on the bare mudflat.
S9: Determine vegetation restoration and construction regions and a priority level of each vegetation restoration and construc- tion region according to the spatial distribution map of the prob- ability of the pioneer vegetation appearing on the bare mudflat.
The bare mudflat distribution intervals with high probability of the pioneer vegetation appearing are determined as vegetation restoration and construction regions. Specifically, a probability threshold may be used for screening, and regions greater than the probability threshold are selected as vegetation restoration and construction regions. There is not necessarily only one vegetation restoration and construction region. Therefore, the vegetation restoration and construction regions are prioritized according to the level of probability, and regional vegetation with high proba- bility is easier to be restored or constructed, and is preferen- tially restored or constructed.
In the present invention, a normalized difference vegetation index is calculated based on a remote sensing image, spatial dis- tributions of vegetation and bare mudflat are extracted, and a spatial distribution of pioneer vegetation is further extracted. Then, a probability of the pioneer vegetation appearing in each elevation interval on the bare mudflat is calculated in combina- tion with the spatial distributions of the bare mudflat and the pioneer vegetation based on elevation data. And then, the bare mudflat is subjected to regional division according to the eleva- tion intervals to obtain a spatial distribution map of the proba- bility of the pioneer vegetation appearing on the bare mudflat. Finally, vegetation restoration and construction priority regions are determined according to the spatial distribution map of the pioneer vegetation.
According to the method for determining vegetation restora- tion and construction regions of coastal beach wetlands of the present invention, the problem of difficulty in comprehensively investigating and accurately judging coastal wetlands with harsh natural conditions, poor accessibility and complex topography and hydrodynamic conditions according to traditional on-site manual investigation can be solved, a large region suitable for vegeta- tion restoration and construction of coastal wetland beaches can be objectively and comprehensively acquired, a target region for vegetation restoration of coastal wetlands can be provided, the accuracy and effectiveness of coastal wetland vegetation restora- tion projects can be improved, and technical support can be pro- vided for coastal wetland restoration and protection.
As an improvement of the embodiment of the present invention, after S2 and before S3, the method further includes the following step: S21: Acquire a remote sensing reflectance image of a coastal beach wetland region from the remote sensing reflectance image.
In this step, a remote sensing reflectance image of a coastal beach wetland region may be obtained by clipping the remote sens- ing reflectance image according to a boundary of the known coastal beach wetland region.
The remote sensing reflectance image of the coastal beach wetland region may be acquired from the remote sensing reflectance through the following methods: S211: Perform water-land separation on the remote sensing re- flectance image to obtain a remote sensing reflectance image of a preliminarily determined coastal beach wetland region.
In this step, information of a specific band is used to per- form edge detection on the image to detect contours of island reefs and a continent, and clipping is performed to complete wa- ter-land separation.
3212: Perform a mask operation on an edge of the remote sens- ing reflectance image of the preliminarily determined coastal beach wetland region to obtain a remote sensing reflectance image of a coastal beach wetland region.
There may be a problem of edge inaccuracy after the water- land separation. The edge mask can correct the edge of the prelim- inarily determined coastal beach wetland region and reduce the problem of detection errors caused by inaccurate water-land bound- ary separation.
Embodiment 2: The embodiment of the present invention provides a device for determining vegetation restoration and construction regions of coastal beach wetlands. As shown in FIG. 2, the device includes: a data acquisition module 1, configured to acquire a remote sensing image and elevation data of a low-tide period of a re- search region; a pre-processing module 2, configured to pre-process the re- mote sensing image to obtain a remote sensing reflectance image, the pre-processing including geometric correction, spatial clip- ping, radiometric calibration, apparent reflectance calculation, and atmospheric correction; a normalized difference vegetation index extraction module 3, configured to extract a normalized difference vegetation index NDVI of the remote sensing reflectance image using a normalized difference vegetation index method to obtain a normalized differ- ence vegetation index distribution map, the normalized difference vegetation index being obtained by the following formula: NDVT = Pam Pea Pym + Pred , where NDVI is a normalized difference vegetation index, and Pres and pyrg are remote sensing reflectance of a red band and a near-infrared band;
a patch extraction module 4, configured to extract vegetation patches and bare mudflat patches according to the normalized dif- ference vegetation index distribution map, and extract pioneer vegetation patches scattered in the bare mudflat patches according to distributions of the vegetation patches and the bare mudflat patches; an elevation data clipping module 5, configured to clip the elevation data using the bare mudflat patches and the pioneer veg- etation patches respectively to obtain bare mudflat elevation patches and pioneer vegetation elevation patches; an elevation interval division module 6, configured to divide the elevation data into a plurality of elevation intervals accord- ing to a certain height spacing, and calculate an area A; of the bare mudflat elevation patches and an area B: of the pioneer vege- tation elevation patches in each elevation interval respectively, where 1 is a number of the elevation interval; a probability calculation module 7, configured to calculate a probability P; of pioneer vegetation appearing in each elevation interval on a bare mudflat, P:i=A;/{A;+B;); a bare mudflat division and probability assignment module 8, configured to perform regional division on the bare mudflat patch- es according to the elevation intervals to obtain various bare mudflat distribution intervals, and assign the probability P; of the pioneer vegetation appearing in each elevation interval on the bare mudflat into the various bare mudflat distribution intervals to obtain a spatial distribution map of the probability of the pi- oneer vegetation appearing on the bare mudflat; a vegetation restoration and construction region determina- tion module 9, configured to determine vegetation restoration and construction regions and a priority level of each vegetation res- toration and construction region according to the spatial distri- bution map of the probability of the pioneer vegetation appearing on the bare mudflat.
According to the present invention, the problem of difficulty in comprehensively investigating and accurately judging coastal wetlands with harsh natural conditions, poor accessibility and complex topography and hydrodynamic conditions according to tradi-
tional on-site manual investigation can be solved, a large region suitable for vegetation restoration and construction of coastal wetland beaches can be objectively and comprehensively acquired, a target region for vegetation restoration of coastal wetlands can be provided, the accuracy and effectiveness of coastal wetland vegetation restoration projects can be improved, and technical support can be provided for coastal wetland restoration and pro- tection.
In the present invention, the radiometric calibration is per- formed by the following formula: L=Gain*DN+Offset L is apparent radiance in Wm sr um’. DN is a digital gray value of the remote sensing image.
Gain is a gain of an absolute calibration coefficient in Wm? sr pm.
Offset is an offset of the absolute calibration coefficient in Wrmsrt pm, and a vacancy value is 0. The apparent reflectance calculation is performed by the fol- lowing formula: TLD? TX = roa FL ocos& = = where Ora is apparent reflectance at the top of atmosphere.
D is a ratio of an actual Earth-Sun distance to a mean Earth- Sun distance.
Fy is solar spectrum illuminance at the top of atmosphere at the mean Earth-Sun distance in Wm? 1m". 8, is a solar zenith angle.
The device further includes: a coastal beach wetland region acquisition module, configured to acquire a remote sensing reflectance image of a coastal beach wetland region from the remote sensing reflectance image.
The coastal beach wetland region acquisition module further includes: a water-land separation unit, configured to perform water- land separation on the remote sensing reflectance image to obtain a remote sensing reflectance image of a preliminarily determined coastal beach wetland region; an edge mask unit, configured to perform a mask operation on an edge of the remote sensing reflectance image of the preliminar- ily determined coastal beach wetland region to obtain a remote sensing reflectance image of a coastal beach wetland region.
The implementation principles of the device provided by the embodiment of the present invention and the technical effects pro- duced thereby are the same as those of the foregoing method embod- iment. For the sake of brief description, the contents that are not mentioned in the device embodiment may be referred to the cor- responding contents in the foregoing method embodiment 1. It will be clear to those skilled in the art that, for the convenience and brevity of the description, reference will be made to the corre- sponding processes in the above-described method embodiment for the specific working processes of the device and the units de- scribed in the foregoing and will not be repeated here.
The above descriptions are preferred implementations of the present invention. It will be appreciated by those of ordinary skill in the art that numerous improvements and modifications may be made without departing from the principle of the present inven- tion, which fall within the protection scope of the present inven- tion.

Claims (10)

CONCLUSIESCONCLUSIONS 1. Werkwijze voor het bepalen van vegetatieherstel- en construc- tiegebieden van kustwatergebieden, omvattende: Sl: verkrijgen van een teledetectiebeeld en hoogtegegevens van een ebperiode van een onderzoeksgebied; S2: voorbewerking van het teledetectiebeeld om een reflectiebeeld met teledetectie te verkrijgen, waarbij de voorbewerking geometri- sche correctie, ruimtelijke clipping, radiometrische kalibratie, schijnbare reflectieberekening en atmosferische correctie omvat; S3: het extraheren van een genormaliseerde verschilvegetatie-index NDVI van het reflectiebeeld met teledetectie met behulp van een genormaliseerde verschilvegetatie-indexmethode voor het verkrijgen van een genormaliseerde verschilvegetatie-indexdistributiekaart, waarbij de genormaliseerde verschilvegetatie-index wordt verkregen door de volgende formule: NDPT = 2x ~ Pred Pri T Prea , waarin NDVI een genormaliseerde verschilvegetatie-index is, en pRed en pNIR teledetectiereflectie zijn van een rode band en een nabij-infraroodband; S4: het extraheren van vegetatieplekken en kale slikplekken vol- gens de genormaliseerde differentiële vegetatie- indexverdelingskaart, en het extraheren van pioniervegetatieplek- ken verspreid in de kale slikplekken volgens de verdelingen van de vegetatieplekken en de kale slikplekken; S5: het knippen van de hoogtegegevens met behulp van respectieve- lijk de kale slikplekken en de pioniervegetatiegebieden om kale slikplekken en pioniervegetatiehoogteplekken te verkrijgen; S6: het verdelen van de hoogtegegevens in meerdere hoogte- intervallen volgens een bepaalde hoogteafstand, en het berekenen van een oppervlakte A; van de kale slikplekken en een gebied B; van de pioniervegetatiehoogtepunten in elk respectievelijk hoogte- interval, waarbij i een nummer van het hoogte-interval aanduidt; S7: het berekenen van een waarschijnlijkheid P; dat pioniervegeta-A method for determining vegetation recovery and construction areas of coastal water areas, comprising: S1: obtaining a remote sensing image and elevation data of an ebb period of a survey area; S2: preprocessing the remote sensing image to obtain a remote sensing reflection image, the preprocessing including geometric correction, spatial clipping, radiometric calibration, apparent reflection calculation and atmospheric correction; S3: Extracting a normalized difference vegetation index NDVI from the remote sensing reflection image using a normalized difference vegetation index method to obtain a normalized difference vegetation index distribution map, where the normalized difference vegetation index is obtained by the following formula: NDPT = 2x ~ Pred Pri T Prea , where NDVI is a normalized difference vegetation index, and pRed and pNIR are remote sensing reflectivity of a red band and a near-infrared band; S4: extracting vegetation patches and bare mud flats according to the normalized differential vegetation index distribution map, and extracting pioneer vegetation patches distributed in the bare mud flats according to the distributions of the vegetation patches and bare mud flats; S5: clipping the elevation data using the bare mud flats and the pioneer vegetation areas, respectively, to obtain bare mud flats and pioneer vegetation elevation patches; S6: dividing the height data into multiple height intervals according to a certain height distance, and calculating an area A; of the bare swallowing spots and an area B; of the pioneer vegetation heights in each respective height interval, where i denotes a number of the height interval; S7: calculate a probability P; that pioneer vege- tie in elk hoogte-interval op kaal slik verschijnt, Pi=A;/ (A:+B:); S8: het uitvoeren van een regionale verdeling op de kale slikplek- ken volgens de hoogte-intervallen om verschillende verdelingsin- tervallen van het kale slik te verkrijgen, en het toewijzen van de waarschijnlijkheid P; van de pioniervegetatie die in elk hoogte- interval op het kale slik verschijnt aan de verschillende verde- lingsintervallen van het kale slik om een ruimtelijke versprei- dingskaart te verkrijgen van de kans dat de pioniervegetatie op het kale slik verschijnt; SY: het bepalen van vegetatieherstel- en aanleggebieden en een prioriteitsniveau van elk vegetatieherstel- en aanleggebied vol- gens de ruimtelijke verspreidingskaart van de kans dat de pionier- vegetatie op het kale slik verschijnt.tie appears in each height interval on bare mud, Pi=A;/ (A:+B:); S8: perform a regional distribution on the bare mud spots according to the height intervals to obtain different distribution intervals of the bare mud, and assign the probability P; of the pioneer vegetation appearing on the bare mud in each height interval to the different distribution intervals of the bare mud to obtain a spatial distribution map of the probability of the pioneer vegetation appearing on the bare mud; SY: Determining vegetation restoration and construction areas and a priority level of each vegetation restoration and construction area according to the spatial distribution map of the probability of the pioneer vegetation appearing on the bare mud. 2. Werkwijze voor het bepalen van vegetatieherstel- en aanlegge- bieden van kustwatergebieden volgens conclusie 1, waarbij de radi- ometrische kalibratie wordt uitgevoerd door de volgende formule: L=Versterking*DN+Offset L is de schijnbare uitstraling in Wm sr’ am"; DN is een digitale grijswaarde van het teledetectiebeeld; Versterking is een versterking van een absolute kalibratieco- efficiënt in Wim sr} um; Offset is een offset van de absolute kalibratiecoëfficiënt in Wm“ sr um’, en een leegstandswaarde is 0.A method for determining vegetation recovery and construction areas of coastal water areas according to claim 1, wherein the radiometric calibration is performed by the following formula: L=Gain*DN+Offset L is the apparent radiance in Wm sr' am" ; DN is a digital gray value of the remote sensing image; Gain is a gain of an absolute calibration coefficient in Wim sr} um; Offset is an offset of the absolute calibration coefficient in Wm“ sr um', and a vacancy value is 0. 3. Werkwijze voor het bepalen van vegetatieherstel- en aanlegge- bieden van kustwatergebieden volgens conclusie 2, waarbij de bere- kening van de schijnbare reflectie wordt uitgevoerd door de vol- gende formule: TLD’ froa Lg i. FooosE waarin pgs. de schijnbare reflectie is aan de bovenkant van de atmosfeer; D een verhouding is van een werkelijke afstand aarde-zon tot een gemiddelde afstand aarde-zon; Fy, de verlichtingssterkte is van het zonnespectrum aan de bo-The method for determining vegetation recovery and construction areas of coastal water areas according to claim 2, wherein the calculation of the apparent reflectance is performed by the following formula: TLD' froa Lg i. FooosE in which pgs. the apparent reflection is at the top of the atmosphere; D is a ratio of an actual Earth-Sun distance to an average Earth-Sun distance; Fy, the illuminance is from the solar spectrum at the top venkant van de atmosfeer op de gemiddelde afstand aarde-zon in Wem? um; 8, een zenithoek van de zon is.side of the atmosphere at the mean Earth-Sun distance in Wem? um; 8, is a zenith angle of the sun. 4. Werkwijze voor het bepalen van vegetatieherstel- en aanlegge- bieden van kustwatergebieden volgens een van de conclusies 1 tot 3, verder omvattende na S2 en vóór S3: S21: verkrijgen van een reflectiebeeld met teledetectie van een kustwatergebied op basis van het reflectiebeeld met teledetectie.A method for determining vegetation restoration and construction areas of coastal water areas according to any one of claims 1 to 3, further comprising after S2 and before S3: S21: obtaining a remote sensing reflection image of a coastal water area based on the remote sensing reflection image . 5. Werkwijze voor het bepalen van vegetatieherstel- en aanlegge- bieden van kustwatergebieden volgens conclusie 4, waarbij S21 om- vat: S211: het uitvoeren van een water-landscheiding op het reflectie- beeld met teledetectie om een reflectiebeeld met teledetectie te verkrijgen van een voorlopig bepaald kustwatergebied; S212: het uitvoeren van een maskerbewerking op een rand van het reflectiebeeld met teledetectie van het voorlopig bepaalde kustwa- tergebied om een reflectiebeeld met teledetectie te verkrijgen van een kustwatergebied.The method for determining vegetation restoration and construction areas of coastal water areas according to claim 4, wherein S21 comprises: S211: Performing a water-land separation on the remote sensing reflection image to obtain a remote sensing reflection image of a provisionally determined coastal water area; S212: Performing a masking operation on an edge of the remote sensing reflection image of the predetermined coastal water area to obtain a remote sensing reflection image of a coastal water area. 6. Apparaat voor het bepalen van vegetatieherstel- en aanleggebie- den van kustwatergebieden, omvattende: een gegevensverwervingsmodule, geconfigureerd om een beeld met teledetectie en hoogtegegevens van een ebperiode van een onder- zoeksgebied te verwerven; een voorbewerkingsmodule, geconfigureerd om het beeld met telede- tectie voor te verwerken om een reflectiebeeld met teledetectie te verkrijgen, waarbij de voorbewerking geometrische correctie, ruim- telijke clipping, radiometrische kalibratie, schijnbare reflectie- berekening en atmosferische correctie omvat; een genormaliseerde differentiële vegetatie-indexextractiemodule, geconfigureerd om een genormaliseerde verschilvegetatie-index NDVI van het reflectiebeeld met teledetectie te extraheren met behulp van een genormaliseerde verschilvegetatie-indexmethode om een genormaliseerde differentiële vegetatie-indexdistributiekaart te verkrijgen, waarbij de genormaliseerde verschilvegetatie-index wordt verkregen door de volgende formule: NDT = Par Pre Pit Pred | waarin NDVI een genormaliseerde verschilvegetatie-index is, en Pred en pyi= teledetectiereflectie zijn van een rode band en een nabij- infraroodband;Apparatus for determining vegetation restoration and construction areas of coastal water areas, comprising: a data acquisition module configured to acquire a remote sensing image and elevation data of an ebb period of a survey area; a pre-processing module configured to pre-process the remote sensing image to obtain a remote sensing reflection image, the pre-processing including geometric correction, spatial clipping, radiometric calibration, apparent reflectance calculation, and atmospheric correction; a normalized differential vegetation index extraction module configured to extract a normalized differential vegetation index NDVI from the remote sensing reflection image using a normalized differential vegetation index method to obtain a normalized differential vegetation index distribution map, where the normalized differential vegetation index is obtained by following formula: NDT = Par Pre Pit Pred | where NDVI is a normalized differential vegetation index, and Pred and pyi = remote sensing reflectivity of a red band and a near-infrared band; een patchextractiemodule, geconfigureerd om vegetatieplekken en kale slikplekken te extraheren volgens de genormaliseerde diffe-a patch extraction module, configured to extract vegetation patches and bare mud patches according to the normalized different rentiële vegetatie-indexverdelingskaart, en pioniervegetatieplek-rential vegetation index distribution map, and pioneer vegetation site ken te extraheren die verspreid zijn in de kale slikplekken vol-to extract germs that are scattered in the bare swallowing sites gens de verdelingen van de vegetatieplekken en de kale slikplek- ken; een module voor het uitknippen van hoogtegegevens, geconfigureerd om de hoogtegegevens te clippen met behulp van respectievelijk de kale slikplekken en de pioniervegetatieplekken om kale slikverho-according to the distributions of the vegetation patches and the bare mud flats; an elevation data clipping module, configured to clip the elevation data using the bare mudflats and pioneer vegetation patches, respectively, to extract bare mudflat elevations gingsplekken en pioniervegetatieplekken te verkrijgen; een hoogte-interval deelmodule, geconfigureerd om de hoogtegege- vens te verdelen in meerdere hoogte-intervallen volgens een be- paalde hoogteafstand, en een gebied A; te berekenen van de kale slikplekken en een gebied B; van de pioniervegetatiehoogtepunten in respectievelijk elk hoogte-interval, waarbij i een getal is van het hoogte-interval; een waarschijnlijkheidsberekeningsmodule, geconfigureerd om een waarschijnlijkheid P; te berekenen dat pioniervegetatie voorkomt in elk hoogte-interval op kaal slik, P;i=A;/ ({B;+B;);to obtain breeding sites and pioneer vegetation sites; a height interval submodule configured to divide the height data into a plurality of height intervals according to a certain height distance, and an area A; to calculate the bald swallowing spots and an area B; of the pioneer vegetation heights in each height interval, respectively, where i is a number of the height interval; a probability calculation module configured to have a probability P; calculate that pioneer vegetation occurs in every height interval on bare mudflats, P;i=A;/ ({B;+B;); een kale slik-verdeling en waarschijnlijkheidstoewijzingsmodule, geconfigureerd om regionale indelingen uit te voeren op de kale slikplekken volgens de hoogte-intervallen om verschillende kale slikverdelingsintervallen te verkrijgen, en de waarschijnlijkheid P, toe te wijzen dat de pioniervegetatie in elk hoogte-interval op het kale slik verschijnt in de verschillende verspreidingsinter- vallen van het kaal slik om een ruimtelijke verspreidingskaart te krijgen van de kans dat de pioniervegetatie op het kaal slik ver- schijnt; een vegetatieherstel- en aanleggebiedbepalingsmodule, geconfigu-a bare mudflat distribution and probability mapping module, configured to perform regional divisions on the bare mud flats according to the elevation intervals to obtain different bare mudflat distribution intervals, and assign the probability P, that the pioneer vegetation in each elevation interval on the bare mud appears in the different distribution intervals of the bare mud to obtain a spatial distribution map of the probability of the pioneer vegetation appearing on the bare mud; a vegetation recovery and construction area determination module, configured reerd om vegetatieherstel- en aanleggebieden te bepalen en een prioriteitsniveau van elk vegetatieherstel- en aanleggebied vol- gens de ruimtelijke verspreidingskaart van de waarschijnlijkheid dat de pioniervegetatie op het kale slik verschijnt.determined to determine vegetation restoration and construction areas and a priority level of each vegetation restoration and construction area according to the spatial distribution map of the probability of the appearance of the pioneer vegetation on the bare mud. 7. Apparaat voor het bepalen van vegetatieherstel- en aanleggebie- den van kustwatergebieden volgens conclusie 6, waarbij de radiome- trische kalibratie wordt uitgevoerd door de volgende formule: L=Versterking*DN+Offset L is de schijnbare uitstraling in Wm? srt um"; DN is een digitale grijswaarde van het teledetectiebeeld; Versterking is een versterking van een absolute kalibratieco- efficiënt in Wim sr tum; Offset is een offset van de absolute kalibratiecoëfficiënt in Wm?-sr'um?', en een leegstandswaarde is 0.Apparatus for determining vegetation restoration and construction areas of coastal water areas according to claim 6, wherein the radiometric calibration is performed by the following formula: L=Gain*DN+Offset L is the apparent radiance in Wm? srt um"; DN is a digital gray value of the remote sensing image; Gain is a gain of an absolute calibration coefficient in Wim sr tum; Offset is an offset of the absolute calibration coefficient in Wm?-sr'um?', and a vacancy value is 0. 8. Inrichting voor het bepalen van vegetatieherstel- en aanlegge- bieden van kustwatergebieden volgens conclusie 7, waarbij de bere- kening van de schijnbare reflectie wordt uitgevoerd door de vol- gende formule:Apparatus for determining vegetation recovery and construction areas of coastal water areas according to claim 7, wherein the calculation of the apparent reflectance is performed by the following formula: TLE ee Ny Tos F, cos a waarin pres de schijnbare reflectie is aan de bovenkant van de atmosfeer; D een verhouding is van een werkelijke afstand aarde-zon tot een gemiddelde afstand aarde-zon; Fo de verlichtingssterkte is van het zonnespectrum aan de bo- venkant van de atmosfeer op de gemiddelde afstand aarde-zon in Wm £ um;TLE ee Ny Tos F, cos a where pres is the apparent reflection at the top of the atmosphere; D is a ratio of an actual Earth-Sun distance to an average Earth-Sun distance; Fo is the illuminance of the solar spectrum at the top of the atmosphere at the mean Earth-Sun distance in Wm £ um; 8. een zenithoek van de zon is.8. is a zenith angle of the sun. 9. Inrichting voor het bepalen van vegetatieherstel- en aanlegge- bieden van kustwatergebieden volgens een van de conclusies 6 tot 8, verder omvattende: eeri acquisitiemodule voor het kustwatergebied, geconfigureerd om een reflectiebeeld met teledetectie van een kustwatergebied te verwerven uit het reflectiebeeld met teledetectie.Apparatus for determining vegetation restoration and construction areas of coastal water areas according to any one of claims 6 to 8, further comprising: a coastal water area acquisition module configured to acquire a remote sensing reflection image of a coastal water area from the remote sensing reflection image. 10. Inrichting voor het bepalen van vegetatieherstel- en aanlegge- bieden van kustwatergebieden volgens conclusie 9, waarbij de ac- quisitiemodule voor kustwatergebieden omvat:Apparatus for determining vegetation restoration and construction areas of coastal water areas according to claim 9, wherein the coastal water area acquisition module comprises: een water-landscheidingseenheid, geconfigureerd om water- landscheiding uit te voeren op het reflectiebeeld met teledetectie om een reflectiebeeld met teledetectie te verkrijgen van een voor- lopig bepaald kustwatergebied; een randmaskereenheid, geconfigureerd om een maskerbewerking uit te voeren op een rand van het reflectiebeeld op afstand van het vooraf bepaalde kustwatergebied om een reflectiebeeld op afstand te verkrijgen van een kustwatergebied.a water-land separation unit configured to perform water-land separation on the remote sensing reflection image to obtain a remote sensing reflection image of a predetermined coastal water area; an edge masking unit configured to perform a masking operation on an edge of the reflection image remote from the predetermined coastal water area to obtain a remote reflection image of a coastal water area.
NL2031646A 2021-04-23 2022-04-20 Method and device for determining vegetation restoration and construction regions of coastal beach wetlands NL2031646B1 (en)

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110439006.9A CN113034519B (en) 2021-04-23 2021-04-23 Method and device for determining vegetation recovery construction area of coastal beach wetland

Publications (2)

Publication Number Publication Date
NL2031646A true NL2031646A (en) 2022-10-31
NL2031646B1 NL2031646B1 (en) 2023-11-27

Family

ID=76457817

Family Applications (1)

Application Number Title Priority Date Filing Date
NL2031646A NL2031646B1 (en) 2021-04-23 2022-04-20 Method and device for determining vegetation restoration and construction regions of coastal beach wetlands

Country Status (2)

Country Link
CN (1) CN113034519B (en)
NL (1) NL2031646B1 (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117036777A (en) * 2023-07-04 2023-11-10 宁波大学 Mud flat extraction method based on hyperspectral data

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114330959A (en) * 2021-09-28 2022-04-12 河海大学 Method for evaluating suitable planting area for growth of suaeda salsa on basis of tidal flat terrain

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2018105046A (en) * 2016-12-27 2018-07-05 富士通株式会社 Soil erosion zone estimation method and soil erosion zone estimation apparatus
CN110059553B (en) * 2019-03-13 2020-11-10 中国科学院遥感与数字地球研究所 Method for judging and identifying vegetation abnormal features in landslide stage by using optical remote sensing image
CN111693463B (en) * 2020-06-10 2022-10-11 中国科学院空天信息创新研究院 Antarctic peninsula optimized lichen coverage index extraction method
CN112396229B (en) * 2020-11-16 2022-10-18 中国科学院东北地理与农业生态研究所 Region division method for natural or artificial recovery of degraded marsh wetland vegetation

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
ANONYMOUS: "Radiometric Correction of Satellite Images", 1 January 2016 (2016-01-01), pages 1 - 4, XP093079286, Retrieved from the Internet <URL:https://www.microimages.com/documentation/TechGuides/82RadiometricCorr.pdf> [retrieved on 20230906] *
PETUS CAROLINE ET AL: "Monitoring temporal dynamics of Great Artesian Basin wetland vegetation, Australia, using MODIS NDVI", ECOLOGICAL INDICATORS, ELSEVIER, AMSTERDAM, NL, vol. 34, 15 May 2013 (2013-05-15), pages 41 - 52, XP028689676, ISSN: 1470-160X, DOI: 10.1016/J.ECOLIND.2013.04.009 *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117036777A (en) * 2023-07-04 2023-11-10 宁波大学 Mud flat extraction method based on hyperspectral data

Also Published As

Publication number Publication date
CN113034519B (en) 2021-08-31
NL2031646B1 (en) 2023-11-27
CN113034519A (en) 2021-06-25

Similar Documents

Publication Publication Date Title
NL2031646B1 (en) Method and device for determining vegetation restoration and construction regions of coastal beach wetlands
Solano et al. A methodology based on GEOBIA and WorldView-3 imagery to derive vegetation indices at tree crown detail in olive orchards
Weiss et al. Application of NOAA-AVHRR NDVI time-series data to assess changes in Saudi Arabia's rangelands
Aurin et al. Spatially resolving ocean color and sediment dispersion in river plumes, coastal systems, and continental shelf waters
Chu et al. Spatio-temporal water quality mapping from satellite images using geographically and temporally weighted regression
Lenney et al. The status of agricultural lands in Egypt: the use of multitemporal NDVI features derived from Landsat TM
Greenberg et al. Shadow allometry: Estimating tree structural parameters using hyperspatial image analysis
Thiemann et al. Lake water quality monitoring using hyperspectral airborne data—A semiempirical multisensor and multitemporal approach for the Mecklenburg Lake District, Germany
Sheng et al. Quantitative dynamic flood monitoring with NOAA AVHRR
CN111832518B (en) Space-time fusion-based TSA remote sensing image land utilization method
Román-Rivera et al. A synthetic review of remote sensing applications to detect nearshore bars
CN113537018A (en) Water and soil conservation monitoring method based on multi-temporal satellite remote sensing and unmanned aerial vehicle technology
CN113033670A (en) Method for extracting rice planting area based on Sentinel-2A/B data
Wu et al. Validation of synthetic daily Landsat NDVI time series data generated by the improved spatial and temporal data fusion approach
CN113327214B (en) Continuous time series water body remote sensing mapping method
CN114627087B (en) Automatic detection method and system for ground feature change of multi-temporal satellite remote sensing image
CN113744249B (en) Marine ecological environment damage investigation method
CN113379759A (en) Automatic water body extraction method for optical remote sensing satellite image
Elnaggar et al. Monitoring Spatial and Temporal Changes of Urban Growth in Dakahlia Governorate, Egypt, by Using Remote Sensing and GIS Techniques.
Feng et al. Long time series (1984–2020) of albedo variations on the Greenland ice sheet from harmonized Landsat and Sentinel 2 imagery
Le Hung et al. Application of remote sensing technique for drought assessment based on normalized difference drought index, a case study of Bac Binh district, Binh Thuan province (Vietnam)
Kennaway et al. Mapping land cover and estimating forest structure using satellite imagery and coarse resolution lidar in the Virgin Islands
Han et al. Detection of change in vegetation in the surrounding desert areas of Northwest China and Mongolia with multi-temporal satellite images
CN115661675B (en) Method, system, equipment and storage medium for identifying earthquake landslide in cloudy region by remote sensing
Xiao et al. A Novel Image Fusion Method for Water Body Extraction Based on Optimal Band Combination.