Vegetation loss direction identification method based on multi-remote-sensing index trend

Abstract

The present invention discloses a vegetation loss direction identification method based on a multi-remote-sensing index trend. The method comprises: calculating a temporal similarity of vegetation indexes between each year and a beginning year by using a JM distance to generate a track of temporal similarity of vegetation indexes; extracting a potential vegetation loss region according to a variation of the temporal similarity of the vegetation index, so as to define a region where the vegetation index is significantly decreased and an impervious surface index is significantly increased as a vegetation loss region; and on this basis, finally determining different vegetation loss directions such as urbanization, desertification and wetland formation according to a water body index and a bare soil index trend feature. In the method, the vegetation change region is determined by using the variation of the temporal similarity, and further, the vegetation loss direction is determined according to multiple remote sensing indexes, without depending on manual intervention for threshold setting, so that the method has the characteristics of high robustness, high classification precision, high automation and storing anti-interference ability, and so on.

Description

technical field [0001] The invention relates to the field of remote sensing information processing, in particular to an automatic identification method for vegetation loss and loss based on time series similarity variation and remote sensing index variation trend. Background technique [0002] Land use / cover change is the result of the interaction between human and nature. Over the past 30 years, with the rapid development of China's economy, land use / cover change has shown the following distinctive features: (1) urbanization, the scale of urban land use has increased, and high-quality cultivated land resources have been occupied; (2) land desertification, On the one hand, after the rural population moved to the cities, arable land was seriously abandoned; on the other hand, the ecological environment in the arid and semi-arid areas of Northwest China was relatively fragile. desertification of the area. Rapid real-time monitoring of land use / cover change is very important ...

AI Technical Summary

Problems solved by technology

The disadvantage of this method is that errors in land use / cover classification have accumulated over the years, and data consistency is relatively poor, making it difficult to achieve continuous automatic monitoring over a large area for many years

With the vigorous development of land use / cover change monitoring technology based on time-series remote sensing data, the following aspects still need to be further analyzed and discussed: (1) Desertification is an important process of land use / cover change. It has received attention since then, but the technical method of making full use of time-series remote sensing data is still rare; (2) The algorithm of time-series remote sensing monitoring technology with the theme of urbanization and forest disturbance monitoring is relatively complicated, and the degree of automation needs to be improved; (3) At present, considering Change detection methods for various land use / cover change types are mostly based on data-driven models, which are difficult to reveal the change process well, and relatively lack geographical significance

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