Earth cover identification and area estimation method based on cloud platform and random forest

Abstract

The invention provides a ground cover classification and area calculation method based on a cloud platform and a random forest, a set of multiband remote sensing data preprocessing technology based on MOD09A1 and MCD12Q1 is formed based on a Google Earth Engine cloud platform, and the method mainly comprises the steps of data set selection, data quality evaluation, data cutting, multiband remote sensing data set band synthesis and the like. And a random forest surface cover classification model based on multi-band remote sensing data is constructed, an image reduction function and image spatial-temporal feature information are combined, surface covers in a large range can be simply, conveniently, efficiently and quickly identified in a cloud computing mode, and the area of the regional surface covers is calculated. According to the invention, by fully utilizing the strong computing power of the cloud platform, the land coverings in a large area can be quickly classified, the area can be estimated, and the change trend of the land coverings in a time sequence can be counted.

Description

technical field [0001] The invention belongs to the technical field of cloud computing and machine learning, and in particular relates to a method for identifying and estimating area of ​​ground cover based on a cloud platform and a random forest. Background technique [0002] Changes in surface cover are inseparable from changes in the natural environment and human activities, and are one of the important basic data for the study of climate change, ecological environment conditions, and the causes of geological disasters. In recent decades, with the continuous progress of science and technology and the continuous launch of remote sensing satellites, the temporal and spatial resolution of remote sensing images have been greatly improved, and the amount of remote sensing-related data has also increased geometrically, which makes the use of remote sensing images. Satellite remote sensing makes it possible to continuously monitor changes in land cover. However, there are still...

AI Technical Summary

Problems solved by technology

[0003] (1) Low efficiency of remote sensing data processing

The traditional download-use method needs to spend a lot of time downloading data in a large-space storage medium, which cannot meet the needs of various industries for fast and efficient realization of large-scale, high-precision, and long-term remote sensing monitoring;

[0004] (2) The accuracy of computer interpretation method is not high

The existing computer interpretation method is mainly based on the threshold method, which is difficult to identify the ground cover with similar spectral characteristics or similar colors, and is often prone to misclassification;

[0005] (3) Interpretation method is not universal

When the common interpretation methods are applied in different areas, professional technicians need to adjust the interpretation parameters according to the terrain and landform, which greatly increases the threshold of use and is difficult to promote and apply;

[0006] (4) Local computing power is too low

[0007] (5) The rapid calculation method of the classified area of ​​land cover is cumbersome and the degree of automation is low

[0008] The area of ​​land cover is an important indicator for studying and analyzing regional environmental change trends. Existing professional software needs to process images in advance, create new attribute fields, and finally use computational geometry functions for calculations. There are many processes and the whole process is uniform. It is a manual operation, and it is difficult to achieve multi-category land cover area statistics

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