Rapid monitoring method for aquatic plants in shallow lake based on HJ-CCD images

Abstract

The invention provides a rapid monitoring method for aquatic plants in a shallow lake based on images of a resource environment moonlet No.1 (HJ-CCD). The method comprises the following steps: 1) acquiring HJ-CCD images of a researched region and preprocessing the images; 2) calculating sensitive characteristic indexes of emergent aquatic plants according to the HJ-CCD images of the researched region; 3) subjecting the HJ-CCD images of the researched region to principal component transformation so as to obtain sensitive characteristics of floating-leaved plants; 4) subjecting the HJ-CCD images of the researched region to tasseled cap transformation so as to obtain sensitive characteristic indexes of submerged plants; 5) determining and recognizing thresholds of different aquatic plants according to measured data synchronous to the images of the researched region and bringing forward discrimination conditions; and 6) establishing a decision tree classification model according to the discrimination conditions and generating a shallow lake aquatic plant monitoring result graph. The method can realize rapid, real-time, large-scale and high-precision recognition and monitoring of spatial distribution of aquatic plants of different kinds.

Description

technical field [0001] The invention belongs to the field of remote sensing applications and relates to a remote sensing rapid monitoring method for aquatic plants in shallow lakes. Background technique [0002] Aquatic plants are an important part of the lake ecosystem, as well as an important regulator and indicator of the lake ecosystem. The real-time and rapid monitoring of aquatic plants has guiding significance for the evaluation of the ecological function of lake ecosystems, and at the same time, it has important practical significance for the restoration of turbid lake ecosystems and the salvage of aquatic plants. [0003] At present, the monitoring methods of aquatic plants still use artificial quadrats and line transect survey methods, which are time-consuming and laborious, with limited monitoring scope and sample size, and difficult monitoring (many places where aquatic plants grow are difficult for people to reach), and cannot be quickly, real-time, large-scale....

AI Technical Summary

Problems solved by technology

[0004] In order to solve the problems and deficiencies of traditional aquatic plant monitoring methods such as representativeness and poor timeliness; time-consuming and laborious, limited monitoring range and sample size, and difficult monitoring, the present invention provides a rapid monitoring of aquatic plants in shallow lakes based on HJ-CCD images Method, described aquatic plant comprises emergent plant, floating leaf plant and submerged plant, and it comprises the following steps:

Images