Method for responding to surface temperature of afforestation based on multi-source remote sensing and energy balance model

Abstract

The invention discloses a method for responding to surface temperature of afforestation based on a multi-source remote sensing and energy balance model. The method comprises the following steps: 1) collecting available remote sensing data, climate data and land coverage data; 2) by utilizing a spatial analysis method, generating a space-time distribution diagram of the unchanged man-made forest, the natural forest and the open land with efficient resolution; 3) carrying out scale conversion and matching on the collected data, unifying the resolution, and carrying out space-time change simulation of afforestation and change analysis of related parameters based on a grid sampling scale moving window space sampling method; and 4) constructing an afforestation response surface temperature distinguishing model integrating a surface energy balance equation and an internal biophysical theory. The multi-source remote sensing data, the climate data and the energy balance model are integrated to evaluate the regional temperature response mechanism of afforestation, so that the model responding to temperature change is more comprehensive and reliable, and an important scientific evaluation basis is provided for implementation of management measures of reasonable afforestation.

Description

Technical field [0001] The present invention is in response to changes in forest Field temperature effect model, particularly to a method based on multiple RS and energy balance model in response planting surface temperature. Background technique [0002] How to accurately quantify the biophysical mechanisms of spatial and temporal distribution of afforestation in response to temperature changes help to understand the forest area of ​​climate change regulation. The traditional methods based on changes in land-use statistics and field observations is typically used to detect the temperature change in response to the forest. However, due to weather or field distribution sites are sparsely laid, can not be located temporal variation of temperature forested dynamic position accurately. It as a most convincing way, especially for a small area, but the lack of mechanism analysis. Climate models have been widely used to study the biophysical feedback mechanism for the forestation of cli...

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Problems solved by technology

However, how the changes in land surface conditions during afforestation and its regional and local albedo radiative feedbacks, energy redistribution and other biophysical energy balance mechanisms and climate feedbacks have yet to be evaluated.

In summary, on the one hand, the limited availability of medium- and high-resolution afforestation data has hindered the accurate quantification of the impact of temperature change, and on the other hand, it is still unclear which biophysical factors are responsible for the regional temperature change

[0004] Conventional observation methods cannot realize the spatial and temporal scale expansion of afforestation and its response to regional temperature changes. First, there is a lack of accurate temporal and spatial information on surface changes. Second, the singleness of observation methods and methods for responding to temperature changes, such as using only remote sensing data combined with some spatial Statistical analysis and quantification methods ignore many key parameters of surface energy balance and the effects of internal biophysical mechanisms on ground temperature. Limited coverage and poor fit with the distribution of forest changes hinder dynamic monitoring and response at provincial and higher scales

According to many existing climate models and models used for forest response to temperature changes, the global climate model has many limitations in local application, and its resolution is too coarse to be suitable for local estimation; regional climate models need to adjust many parameters to Localization, using these methods alone, has highlighted their own shortcomings, so it is difficult to give full play to the characteristics of various observation methods and methods at the local scale

Coarse-resolution MODIS land cover data are usually used for large-scale research due to low data resolution, and cannot accurately locate forest changes that occur at small regional scales

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