Forest interannual phenology monitoring method based on multi-source remote sensing

Abstract

The invention discloses a forest interannual phenology monitoring method based on multi-source remote sensing, which comprises the following steps: firstly, collecting all available satellite remote sensing images of which the cloud cover is less than 80%, and then correcting an integration method of different satellite remote sensing images to improve the space and spectrum matching degree of different sensors; then generating a daily vegetation index curve by using an improved continuous change detection and classification model; and finally, based on the daily synthetic image, using a logistic regression model to test the enhanced vegetation index, the normalized vegetation index and the surface water body index to extract the optimal forest interannual SOS. According to the invention, the integration method of different satellite data is improved, and the observation frequency is increased; an MCCDC model is proposed, radiation differences are taken into consideration, a model algorithm is optimized, the calculation time is shortened while the precision is ensured, and finally a daily clear cloudless remote sensing image is generated; 3 vegetation indexes are introduced to estimate forest interannual SOS, and the difference of different indexes in evaluating forest SOS is evaluated.

Description

technical field [0001] The invention belongs to the technical field of forest phenology monitoring, and in particular relates to an interannual forest phenology monitoring method based on multi-source remote sensing. Background technique [0002] In recent decades, remote sensing has gradually become an effective means of monitoring forest phenology dynamics due to the limitations of large-scale monitoring and mapping of ground phenology observation networks. Among them, some low spatial resolution sensors are widely used in phenological information extraction due to their high temporal resolution, such as Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectrometer (MODIS) with high temporal resolution and The ability to obtain remote sensing data with a spatial resolution of 500 meters to 1100 meters has the advantage of large-scale observation. On this basis, domestic and foreign scholars have proposed many models and algorithms to estimat...

AI Technical Summary

Problems solved by technology

[0009] (2) The premise of accurate estimation by the CCDC model is the need for a large number of clear Landsat observations. If there is continuous rainy and cloudy weather, the problem of model overfitting or underfitting may occur, which is not universal, and the CCDC model Differences in radiation between different sensors are not taken into account

Therefore, the CCDC model judges each observation value pixel by pixel, and iteratively updates the model parameters, which requires a large amount of calculation and low work efficiency. For land plots with constant land cover types in the Landsat scene, the actual observation value and the model parameters are compared pixel by pixel. Model predictions are time consuming

Images