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Dynamic monitoring method for new plaques of spartina alterniflora

A technology of Spartina alterniflora and dynamic monitoring, which is applied in image data processing, instruments, character and pattern recognition, etc., and can solve the problems of increasing plaque recognition and monitoring errors and uncertainties

Active Publication Date: 2019-07-19
CAPITAL NORMAL UNIVERSITY
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  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Most of the high-resolution satellite data have different spatial resolutions. Direct use of these data will increase the error and uncertainty of plaque identification and monitoring. In order to solve the above shortcomings, the present invention will propose a multi-platform high-resolution satellite data A large-scale, non-contact method for dynamic monitoring of newborn plaques of Spartina alterniflora based on fusion technology

Method used

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  • Dynamic monitoring method for new plaques of spartina alterniflora
  • Dynamic monitoring method for new plaques of spartina alterniflora
  • Dynamic monitoring method for new plaques of spartina alterniflora

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Experimental program
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Effect test

Embodiment 1

[0070] see figure 1 ;

[0071] The dynamic monitoring method of the new patch of Spartina alterniflora comprises the following steps:

[0072] S1. Identification of potential colonization areas of new-born patches: Using medium-resolution remote sensing satellite images, according to the method of visual interpretation, and fully considering the temporal difference between Spartina alterniflora and other vegetation, identify Spartina alterniflora from sea to land The range boundary of Spartina alterniflora was combined with the tidal level data to obtain the intertidal zone range, so that the intertidal zone area where the range boundary of Spartina alterniflora was not identified was used as the potential colonization area of ​​the new patch;

[0073] S2. Multi-source, multi-temporal and high-spatial-resolution satellite image collection: collect GF-1, GF-2, SPOT-6, or WorldView-2 images covering the above-mentioned potential colonization areas in the same peak growth season...

Embodiment 2

[0084] see figure 2 , based on Embodiment 1, the difference is that;

[0085] The image data preprocessing process mentioned in S3 is:

[0086] 1) Radiometric calibration: perform radiometric calibration on multispectral data and panchromatic data according to the Apply Gain and Offset tool provided by ENVI (Complete Remote Sensing Image Processing Platform);

[0087] 2) Atmospheric correction: use the FLAASH atmospheric correction module in the ENVI (Complete Remote Sensing Image Processing Platform) software to perform atmospheric correction on high-resolution multispectral images;

[0088] 3) Orthorectification: use DEM data to correct the image space distortion caused by the elevation fluctuation of the image, and generate a multi-center projection plane orthorectification image;

[0089] 4) Image fusion: use the Gram-Schmidt method to fuse the multi-source, multi-temporal, high-spatial-resolution satellite images to obtain the fused multi-temporal, multi-source, high-r...

Embodiment 3

[0097] see image 3 , based on Embodiment 1 or 2, the difference is that;

[0098] The FSRCNN model mentioned in S4 is divided into five layers, which are feature extraction layer, shrinkage layer, mapping layer, expansion layer and reconstruction layer. The first four layers are convolution processing, which is used to extract low-dimensional data from high-dimensional data. ; and the last part of the reconstruction layer is deconvolution processing, which is used to map low-dimensional data into high-dimensional output, respectively use Conv(fi,ni,ci) to represent convolution processing, and DeConv(fi,ni,ci) to represent inverse Convolution processing, where the variables fi, ni, and ci represent the filter size, the number of filters, and the number of channels, respectively.

[0099] In this step, the high-score images of different resolutions (Table 1) are unified to a resolution of 0.5m through super-resolution reconstruction. The present invention uses the FSRCNN deep...

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Abstract

The invention discloses a dynamic monitoring method for the new plaques of spartina alterniflora, and belongs to the technical field of environment monitoring. The method mainly aims to solve the problems that the estuary wetland environment is complex, the field monitoring and measurement work of the new plaques of spartina alterniflora is difficult to carry out, and more medium-spatial resolution satellites can only meet the monitoring requirement of a landscape scale and cannot identify the new small plaques due to the limitation of the spatial resolution of the medium-spatial resolution satellites; by uasing a single-platform high-resolution satellite, due to a revisit period and weather restrictions of cloudy and rainy estuary, the dynamic change monitoring of new plaques is not facilitated, while the multi-platform high-resolution satellite has different spatial resolutions, the accurate new plaque identification and measurement cannot be obtained when the multi-platform high-resolution satellite is directly and respectively used, so that the large-range and non-contact dynamic monitoring method for the new plaque of the spartina alterniflora based on the multi-platform high-resolution satellite data fusion technology is provided.

Description

technical field [0001] The invention relates to the technical field of environmental monitoring, in particular to a dynamic monitoring method for new patches of Spartina alterniflora. Background technique [0002] Coastal wetlands are located in the ecological ecotone between terrestrial ecosystems and marine ecosystems. They are rich in biodiversity and provide important ecological service functions. At the same time, due to the dual impacts of nature and humans, coastal wetlands are also sensitive and ecologically fragile areas. , Spartina alterniflora is an important alien invasive species in my country's coastal wetlands. It was included in the first batch of invasive species list announced by the former State Environmental Protection Administration in 2003. The rapid spread has greatly reduced the biodiversity of coastal wetlands, caused a large number of native vegetation to die, and exacerbated the degradation of seagrass beds, which has led to serious ecological securi...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06T3/40G06T5/00G06T5/50G06T7/11G06T7/62
CPCG06T5/50G06T7/62G06T3/4053G06T7/11G06T2207/10032G06T2207/30188G06V20/188G06T5/80
Inventor 柯樱海陈盟盟李鹏张双月王明丽吕明苑周德民
Owner CAPITAL NORMAL UNIVERSITY
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