Method and system for quantifying time dynamics of surface temperature in response to forest loss

Abstract

The invention belongs to the technical field of forest loss assessment, and relates to a method and a system for quantifying time dynamics of surface temperature responding to forest loss. The method comprises the following steps: extracting a trend component and a seasonal change component in an LST time sequence, and detecting mutation of the LST trend component; performing space-time matching on the trend component mutation of the LST and a forest loss event; selecting a forest loss pixel and a background forest pixel by adopting a method of combining space with time; and quantizing the long-term response of the LST to the forest loss by adopting the abrupt change characteristic value of the trend component of the LST and the gradual change characteristic value of the trend component. According to the method, the forest loss window and the subsequent temperature response interval thereof are set for each forest loss event in a mode of matching the trend component mutation of the LST with the forest loss event, so that the observed LST dynamic state is only related to the forest loss, and the uncertainty of LST response quantification is reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of forest loss assessment, and in particular, relates to a method and system for quantifying the temporal dynamics of surface temperature response to forest loss. Background Art

[0002] Forest loss affects land surface temperature (LST) by changing the radiative flux and non-radiative processes between the atmosphere and the land surface by altering the physical properties of the land surface, such as surface albedo and roughness. It is generally believed that boreal forest loss will reduce land surface temperature due to reduced downwelling shortwave radiation, while tropical forest loss will increase land surface temperature due to reduced evapotranspiration. Understanding the climate effects and biophysical mechanisms of forest loss is important for mitigating local climate change by guiding terrestrial radiation management and natural climate solutions.

[0003] Satellite remote sensing observations can provide direct evidence for the surface temperature response to forest loss and are widely used to assess the biophysical effects of forest loss at regional and even global scales. Currently, there are three methods for quantifying this biophysical effect based on satellite remote sensing observations:

[0004] (1) Paired comparisons that use space instead of time: For example, the spatial differences in surface temperature between forests with different vegetation coverage or between forests and farmland, or between forests and grasslands are extracted to approximate the impact of deforestation or other land use on local temperature. However, the biophysical effect extracted by this method is an estimate under ideal conditions. Considering the distribution characteristics of patches of different land cover types on the land surface and the differences and complexity of the terrain, the temperature response obtained by this method has certain deviations.

[0005] (2) Methods combining space and time: This type of method focuses on real forest loss events, and regards the difference in surface temperature before and after forest loss as the combined impact of the biophysical effect of forest loss and climate change. The temperature changes of background stable forest pixels during the forest loss period (which can be regarded as the impact of climate change) are eliminated to extract the response of surface temperature to forest loss.

[0006] The method of combining space with time focuses on the static difference before and after forest cover change, confusing the sudden response of LST caused by forest loss events with the slow response of LST caused by subsequent terrain alternation, resulting in the LST response being heavily dependent on the time setting after forest loss in the static comparison. The method of replacing time with space only considers the difference between stable forest and non-forest cover, ignoring the long-term dynamic changes in the LST response caused by forest loss and its subsequent terrain alternation. Due to inconsistent experimental settings or methodological differences in a single method, the state after forest loss or stable non-forest cover involved in existing studies is at different stages of evolution after forest loss, so the signs and magnitudes of LST responses to forest loss quantified by different studies are inconsistent. Summary of the invention

[0007] In order to solve the above technical problems, the present invention provides a method and system for quantifying the temporal dynamics of surface temperature response to forest loss.

[0008] In a first aspect, the present invention provides a method for quantifying the temporal dynamics of surface temperature response to forest loss, comprising:

[0009] Extract trend components and seasonal change components from LST time series, and detect sudden changes in trend components;

[0010] The forest loss pixels that triggered the trend component mutation of LST were selected to evaluate the impact of forest loss on LST, the trend component mutation of LST was matched with the forest loss event in time and space, and the pairing time before and after the loss and the long-term dynamic monitoring period were specified for each forest loss event;

[0011] The method of combining space and time was adopted to select forest loss pixels and background forest pixels of forest loss pixels, and calculate the sudden change characteristic value and gradual change characteristic value of the trend component of LST;

[0012] The mutation characteristic value and gradual characteristic value of the trend component of LST are used to quantify the long-term response of LST to forest loss.

[0013] In a second aspect, the present invention provides a system for quantifying the temporal dynamics of surface temperature response to forest loss, including an extraction detection unit, a matching unit, a selection and analysis unit, and a processing unit;

[0014] Extraction and detection unit, used to extract trend components and seasonal change components in the LST time series, and detect sudden changes in trend components;

[0015] The matching unit is used to select forest loss pixels that trigger the trend component mutation of LST to evaluate the impact of forest loss on LST, match the trend component mutation of LST with the forest loss event in time and space, and specify the pairing time before and after the loss and the long-term dynamic monitoring period for each forest loss event;

[0016] Selection and analysis units are used to select forest loss pixels and background forest pixels of forest loss pixels by using a method combining space and time, and calculate the sudden change characteristic value and gradual change characteristic value of the trend component of LST;

[0017] The processing unit is used to quantify the long-term response of LST to forest loss using the mutation characteristic value of the trend component of LST and the gradual characteristic value of the trend component.

[0018] Based on the above technical solution, the present invention can also be improved as follows.

[0019] Furthermore, the long-term trend in the piecewise linear function LST time series is adopted, and multiple second-order harmonic functions are used to simulate the seasonal variation in the LST time series. Let the LST of the time series be LST t , the trend component is T t , the seasonal group is S t , the residual is e t , the LST of the time series is the sum of the trend component, the seasonal component and the residual part, t is the time, n is the number of LST observations, t=1,...,n, T t i is a linear function simulating the trend component of LST, is the second-order harmonic function of the seasonal component, m is the number of breakpoints, and the trend component of LST is composed of m+1 linear functions T t i Simulation, i is the number of segments of the linear function, i = 1, ..., m + 1, a i is the slope of the linear function of the i-th segment, b i is the intercept of the linear function of the i-th segment. The LST seasonal component consists of multiple second-order harmonic functions fit, q+1 is the number of harmonic functions, is the seasonal breakpoint, and the amplitude of the jth harmonic function is The phase of the jth harmonic function is tan -1 (σ j,h / γ j,h ), γ j,h and σ j,h is the function that determines the amplitude of the jth harmonic function and phase (tan -1 (σ j,h / γ j,h ), h is the order of the harmonic function, λ is the period of the LST time series, the seasonal component is fitted by several second-order harmonic functions, and the trend component of LST is simulated by m+1 linear functions, then:

[0020] LST t =Tt +S t +e t ,t=1,…,n;

[0021]

[0022] Furthermore, the conditions for temporally and spatially matching the trend component mutation of LST with the forest loss event include: the forest loss occurs within the confidence range of the trend component mutation of LST detected by the change detection method, and the time difference between the forest loss and the trend component mutation of LST does not exceed the set number of years; if the detected trend component mutation of LST matches the forest loss event, the forest loss window of the forest loss pixel is defined as the union of the trend component mutation year of LST and the forest loss event year.

[0023] Furthermore, background forest pixels controlled by each forest loss pixel are selected, and the background forest pixels satisfy the following conditions: the background forest pixel is located within the search window within the set distance range from the forest loss pixel, the forest cover change rate of the background forest pixel is less than the set value and is less than the loss percentage of the forest loss pixel, the elevation difference between the background forest pixel and the forest loss pixel is less than the set value, and the number of background forest pixels exceeds the set ratio of all background pixels.

[0024] Furthermore, for each forest loss pixel, the LST change caused by forest loss was obtained by calculating the difference between the LST change value observed in the forest loss pixel and the LST change value observed in the background forest pixel during the same period.

[0025] Furthermore, let the trend component of LST after forest loss be ΔT, the target forest loss pixel be o, the background stable forest pixel be bg, and the trend component of forest loss pixel after loss be T o,after The trend group of forest loss pixels one year before the loss is T_1 o,before The change in the trend component one year after forest loss is ΔT_1, and the annual scale difference between the trend component of the forest loss pixel after the loss and the trend component of the forest loss pixel one year before the loss is δT o The average difference in trend components of all background forest pixels is δT bg , then the time series change of the trend component of LST after forest loss is expressed as ΔT; the change of the trend component one year after forest loss ΔT_1 represents the immediate impact of forest loss on LST, and the calculation formula is:

[0026] ΔT=δT o -δT bg ;

[0027] δT o =T o,after -T_1o,before .

[0028] Furthermore, let the linear slope of the trend component of the forest loss pixel LST before the loss be T_slope o,before The linear slope of the trend component of the forest loss pixel LST after the loss is T_slope o,after The linear slope of the trend component of the forest loss pixel LST changes from before to after the loss as δT_slope o , the linear slope from the loss to the next mutation or the end of the time series is T_slope o,after The linear slope of the trend component of the background forest pixel LST before the loss is T_slope bg,before , the linear slope of the trend component of the background forest pixel LST after loss is T_slope bg,after The difference between the linear slope of the trend component of the background forest pixel LST before and after the loss is δT_slope bg , the forest loss pixel has the same climate signal as the background forest pixel before the loss, that is, T_slope o,before =T_slope bg,before , the linear slope change of the trend component of LST after the loss is ΔT_slope, and the gradual change of the trend component of LST is quantified by the linear slope change of the trend component of LST after the loss, that is:

[0029] ΔT_slope=δT_slope o -δT_slope bg ;

[0030] δT_slope o =T_slope o,after -T_slope o,before .

[0031] The beneficial effects of the present invention are:

[0032] (1) By applying the change detection method to the mechanism of combining space and time, the present invention can distinguish the sudden impact of forest loss on LST and the response of LST to the subsequent evolution of terrestrial vegetation, characterize the long-term dynamic pattern of LST response, and help reconcile the inconsistent results of existing studies on the impact of forest loss;

[0033] (2) The present invention sets a forest loss window and its subsequent temperature response interval for each forest loss event by matching the trend component mutation of LST with the forest loss event, ensuring that the observed LST dynamics are only related to forest loss, which increases the comparability of LST responses between different pixels and reduces the uncertainty of LST response quantification;

[0034] (3) The combined change detection and spatial-temporal quantitative temperature response method of the present invention is generalizable and applicable to any land use and land cover change, and can effectively assess the biophysical effects of these disturbances on the local climate;

[0035] (4) Based on the climate-forest loss feedback, which is climate-specific, loss type-dependent, and time-varying, the present invention helps improve land surface models to more accurately predict the climate, which is of great significance for making correct decisions on intervention measures to address the intensification of climate change. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 A schematic diagram of a method for quantifying the temporal dynamics of surface temperature response to forest loss provided in Example 1 of the present invention;

[0037] Figure 2 It is a schematic diagram of the results of LST change detection;

[0038] Figure 3 This is a simulation diagram of the long-term dynamic response of LST to multiple forest loss types in different climate zones around the world;

[0039] Figure 4 A schematic diagram of a system for quantifying the temporal dynamics of surface temperature response to forest loss provided in Example 2 of the present invention. DETAILED DESCRIPTION

[0040] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0041] Example 1

[0042] As an example, Figure 1 As shown, in order to solve the above technical problems, this embodiment provides a method for quantifying the temporal dynamics of surface temperature response to forest loss, including:

[0043] Extract trend components and seasonal change components from LST time series, and detect sudden changes in trend components;

[0044] The forest loss pixels that triggered the trend component mutation of LST were selected to evaluate the impact of forest loss on LST, the trend component mutation of LST was matched with the forest loss event in time and space, and the pairing time before and after the loss and the long-term dynamic monitoring period were specified for each forest loss event;

[0045] The method of combining space and time was adopted to select forest loss pixels and background forest pixels of forest loss pixels, and calculate the sudden change characteristic value and gradual change characteristic value of the trend component of LST;

[0046] The mutation characteristic value and gradual characteristic value of the trend component of LST are used to quantify the long-term response of LST to forest loss.

[0047] The present invention proposes a method for quantifying the response of LST to forest loss by combining change detection with space and time. Firstly, the change detection method is used to extract the trend component in the LST time series inverted by satellite remote sensing and detect the mutation of the trend component of LST. By matching the mutation of the trend component of LST with the forest loss event in time and space, a pairing time before and after the loss and a long-term dynamic monitoring period are specified for each forest loss event. Secondly, the method of combining space and time is adopted to quantify the long-term response of LST to forest loss by using the mutation and gradual change of the trend component of LST.

[0048] Optionally, use the long-term trend in the piecewise linear function LST time series, use multiple second-order harmonic functions to simulate the seasonal variation in the LST time series, and let the LST of the time series be LST t , the trend component is T t , the seasonal group is S t , the residual is e t , the LST of the time series is the sum of the trend component, the seasonal component and the residual part, t is the time, n is the number of LST observations, t=1,...,n, T t i is a linear function simulating the trend component of LST, is the second-order harmonic function of the seasonal component, m is the number of breakpoints, and the trend component of LST is composed of m+1 linear functions T t i Simulation, i is the number of segments of the linear function, i = 1, ..., m + 1, a i is the slope of the linear function of the i-th segment, b i is the intercept of the linear function of the i-th segment. The LST seasonal component consists of multiple second-order harmonic functions fit, q+1 is the number of harmonic functions, is the seasonal breakpoint, and the amplitude of the jth harmonic function is The phase of the jth harmonic function is tan -1 (σ j,h / γ j,h ), γ j,h and σ j,h is the function that determines the amplitude of the jth harmonic function and phase (tan -1 (σ j,h / γj,h ), h is the order of the harmonic function, λ is the period of the LST time series, the seasonal component is fitted by several second-order harmonic functions, and the trend component of LST is simulated by m+1 linear functions, then:

[0049] LST t =T t +S t +e t ,t=1,…,n;

[0050]

[0051] The improved Breaks For Additive Seasonal and Trend method (improved BEAST) is selected to extract the long-term trend of the LST time series, extract the trend component and seasonal change component in the LST time series, and detect the mutations between the trend component and the seasonal change component. The improved BEAST is applied to the global LST time series observed by MODIS, decomposing the LST into trend and seasonal components, and detecting the first three most significant mutations (i.e., m=q=3), as shown in the attached figure. Figure 2 The result diagram of LST change detection of an example pixel is shown, with t on the horizontal axis and the unit: year, and the time series LST observation value Y on the vertical axis. t Decomposed into trend components T t , Seasonal component S t and remainder e t The detected mutations are shown as vertical lines, and the confidence intervals for these mutations are represented by the time period between the two vertical lines.

[0052] Optionally, the conditions for temporally and spatially matching the trend component mutation of LST with the forest loss event include: the forest loss occurs within the confidence range of the trend component mutation of LST detected by the change detection method, and the time difference between the forest loss and the trend component mutation of LST does not exceed a set number of years; if the detected trend component mutation of LST matches the forest loss event, the forest loss window of the forest loss pixel is defined as the union of the year of the trend component mutation of LST and the year of the forest loss event.

[0053] The forest loss pixels that caused the LST trend mutation were selected to evaluate the impact of forest loss on LST. The forest loss data came from the global high-resolution forest map. The map extracted the global forest range and forest loss and forest growth from 2000 to 2021 through time series analysis of Landsat satellite data. For example, the matching criteria for LST trend mutation and forest loss events are as follows:

[0054] (a) Forest loss occurs within the confidence range of the LST trend abrupt changes detected by the change detection method;

[0055] (b) the time lag between forest loss and abrupt changes in LST trends does not exceed 2 years;

[0056] (c) For a pixel, if the detected LST mutation matches a forest loss event, the forest loss window is defined as the union of the year of the LST trend mutation and the year of the forest loss event. Therefore, the study period for the pixel starts from the year before the loss window and ends at the next mutation or the end of the time series, so that the evolution of the LST response up to 14 years can be explored.

[0057] As attached Figure 3 As shown in Figure 2, the sample pixel experienced deforestation in 2005, and the LST trend component changed suddenly in 2005, so the forest loss window of the pixel spanned from the beginning of 2005 to the end of 2006.

[0058] Optionally, background forest pixels controlled by each forest loss pixel are selected, and the background forest pixels meet the following conditions: the background forest pixel is within a search window within a set distance range from the forest loss pixel, the forest coverage change rate of the background forest pixel is less than a set value and is less than the loss percentage of the forest loss pixel, the elevation difference between the background forest pixel and the forest loss pixel is less than a set value, and the number of background forest pixels exceeds a set ratio of all background pixels.

[0059] Under the assumption that the climate change signal is consistent at the regional scale, the climate signal of the forest loss pixel is estimated by the LST change of the adjacent area with stable forest cover (i.e., background forest pixel). The background forest pixel used as the control for each forest loss pixel should satisfy:

[0060] (1) Located within the search window of 25 km to 50 km from the forest loss pixel;

[0061] (2) having a stable forest land cover, with a forest change percentage less than the minimum of 2% and a forest loss percentage of pixels;

[0062] (3) The elevation difference with the forest loss pixel is less than 100 m. In addition, the number of background forest pixels should exceed 5% of all background pixels to ensure reliable statistical analysis, otherwise, the paired samples are excluded from the evaluation.

[0063] Land cover maps of forest land cover were derived from annual global land cover maps with a spatial resolution of 300 m provided by the ESA Global Land Cover Data, and elevation data were derived from the Shuttle Radar Topography Mission with a resolution of 90 m.

[0064] Optionally, for each forest loss pixel, the LST change caused by forest loss is obtained by calculating the difference between the LST change value observed in the forest loss pixel and the LST change value observed in the background forest pixel during the same period.

[0065] Optionally, let the trend component of LST after forest loss be ΔT, the target forest loss pixel be o, the background stable forest pixel be bg, and the trend component of forest loss pixel after loss be T o,after The trend group of forest loss pixels one year before the loss is T_1 o,before The change in the trend component one year after forest loss is ΔT_1, and the annual scale difference between the trend component of the forest loss pixel after the loss and the trend component of the forest loss pixel one year before the loss is δT o The average difference in trend components of all background forest pixels is δT bg , then the time series change of the trend component of LST after forest loss is expressed as ΔT; the change of the trend component one year after forest loss ΔT_1 represents the immediate impact of forest loss on LST, and the calculation formula is:

[0066] ΔT=δT o -δT bg ;

[0067] δT o =T o,after -T_1 o,before .

[0068] Optionally, let the linear slope of the trend component of the forest loss pixel LST before loss be T_slope o,before The linear slope of the trend component of the forest loss pixel LST after the loss is T_slope o,after The linear slope of the trend component of the forest loss pixel LST changes from before to after the loss as δT_slope o , the linear slope from the loss to the next mutation or the end of the time series is T_slope o,after The linear slope of the trend component of the background forest pixel LST before the loss is T_slope bg,before , the linear slope of the trend component of the background forest pixel LST after loss is T_slope bg,after The difference between the linear slope of the trend component of the background forest pixel LST before and after the loss is δT_slope bg , the forest loss pixel has the same climate signal as the background forest pixel before the loss, that is, T_slope o,before =T_slope bg,before, the linear slope change of the trend component of LST after the loss is ΔT_slope, and the gradual change of the trend component of LST is quantified by the linear slope change of the trend component of LST after the loss, that is:

[0069] ΔT_slope=δT_slope o -δT_slope bg ;

[0070] δT_slope o =T_slope o,after -T_slope o,before .

[0071] The method based on joint change detection and spatial and temporal quantification of the response of LST to forest loss evaluated the long-term dynamic response of LST to multiple forest loss types in different climate zones around the world. The climate zone types include southern hemisphere mid-latitudes (SM, 60°Sˉ20°S), low latitudes (LL, 20°Sˉ20°N), northern hemisphere mid-latitudes (NM, non-northern climate zones above 20°N), and boreal regions (boreal, northern climate zones above 20°N); forest loss types include commodity-driven loss from forest to farmland (sCRO), urbanization (URB), temporary transfer from forest to farmland (shiftAG), forestry management (forestry) and fire (fire). As shown in the attached figure Figure 3 The simulation diagram of the long-term dynamic response of LST to multiple forest loss types in different climate zones around the world is shown. The horizontal axis is the time after the loss, in years, and the vertical axis is the trend component ΔT of LST after forest loss in the southern hemisphere mid-latitudes (SM), low latitudes (LL), northern hemisphere mid-latitudes (NM) and boreal regions (boreal), in K. Permanent forest loss driven by the loss from forest to farmland is the main factor causing warming, causing the largest warming. The trend component mutation ΔT_1 values ​​of the trend component of LST at LL and SM latitudes are +0.39K and +0.41K, respectively; in the long run, ΔT shows an increasing trend. URB usually leads to enhanced warming, and its ΔT_slope is the highest: for example, the ΔT_slope of URB in the northern latitudes is +0.03K / 10y; the linear slope change ΔT_slope of the trend component of LST after the loss of URB in the northern region is +0.08K / 10y.

[0072] Temporary forest loss resulted in multiple dynamic patterns of ΔT. Temporary forest loss in the NM region resulted in attenuated warming. Specifically, temporary transfers from forest to cropland, forestry management, and fire resulted in a sudden increase in ΔT followed by a recovery trend (i.e., ΔT_1>0, ΔT_slope<0), indicating that this warming was attenuated as forests recovered. Among these disturbances, fire showed the most significant abrupt warming (ΔT_1=+0.30K) and the most pronounced decreasing trend (ΔT_slope=-0.14K / 10y). In the boreal region, temporary transfers from forest to cropland resulted in a recovery trend of negative ΔT (i.e., attenuated cooling) due to forest recovery. However, forestry management and fire did not show recovery dynamics. Forestry produced an enhanced cooling effect with a ΔT_slope of -0.16K / 10y; fire initially resulted in attenuated warming, then enhanced cooling, and had a larger decreasing trend (ΔT_slope=-0.34k / 10y) compared with forestry management. Forestry and fires in the boreal region cause ΔT to consistently deviate from zero, suggesting that this region is less resilient compared to low and mid-latitudes.

[0073] By applying the change detection method to the mechanism of combining space and time, the present invention can distinguish the sudden impact of forest loss on LST and the response of LST to the subsequent evolution of terrestrial vegetation, characterize the long-term dynamic pattern of LST response, and help reconcile the inconsistent results of existing studies on the impact of forest loss;

[0074] The present invention sets a forest loss window and its subsequent temperature response interval (i.e., from one year before the loss to the end of the time series or the next sudden change) for each forest loss event by matching the trend component mutation of LST with the forest loss event, ensuring that the observed LST dynamics are only related to forest loss, which increases the comparability of LST responses between different pixels and reduces the uncertainty of LST response quantification;

[0075] The method of combining change detection and spatial and temporal quantitative temperature response of the present invention is generalizable and applicable to any land use and land cover change, and can effectively assess the biophysical effects of these disturbances on the local climate;

[0076] Based on the climate-forest loss feedback being climate specific, loss type dependent, and time-varying, the present invention helps improve land surface models to more accurately predict climate, which is of great significance for making correct decisions on intervention measures against the intensification of climate change.

[0077] Example 2

[0078] Based on the same principle as the method shown in Example 1 of the present invention, as shown in the attached Figure 4As shown, an embodiment of the present invention also provides a system for quantifying the temporal dynamics of surface temperature response to forest loss, including an extraction detection unit, a matching unit, a selection and analysis unit, and a processing unit;

[0079] Extraction and detection unit, used to extract trend components and seasonal change components in the LST time series, and detect sudden changes in trend components;

[0080] The matching unit is used to select forest loss pixels that trigger the trend component mutation of LST to evaluate the impact of forest loss on LST, match the trend component mutation of LST with the forest loss event in time and space, and specify the pairing time before and after the loss and the long-term dynamic monitoring period for each forest loss event;

[0081] Selection and analysis units are used to select forest loss pixels and background forest pixels of forest loss pixels by using a method combining space and time, and calculate the sudden change characteristic value and gradual change characteristic value of the trend component of LST;

[0082] The processing unit is used to quantify the long-term response of LST to forest loss using the mutation characteristic value of the trend component of LST and the gradual characteristic value of the trend component.

[0083] Optionally, use the long-term trend in the piecewise linear function LST time series, use multiple second-order harmonic functions to simulate the seasonal variation in the LST time series, and let the LST of the time series be LST t , the trend component is T t , the seasonal group is S t , the residual is e t , the LST of the time series is the sum of the trend component, the seasonal component and the residual part, t is the time, n is the number of LST observations, t=1,...,n, T t i is a linear function simulating the trend component of LST, is the second-order harmonic function of the seasonal component, m is the number of breakpoints, and the trend component of LST is composed of m+1 linear functions T t i Simulation, i is the number of segments of the linear function, i = 1, ..., m + 1, a i is the slope of the linear function of the i-th segment, b i is the intercept of the linear function of the i-th segment. The LST seasonal component consists of multiple second-order harmonic functions fit, q+1 is the number of harmonic functions, is the seasonal breakpoint, and the amplitude of the jth harmonic function is The phase of the jth harmonic function is tan -1 (σ j,h / γ j,h ), γ j,hand σ j,h is the function that determines the amplitude of the jth harmonic function and phase (tan -1 (σ j,h / γ j,h ), h is the order of the harmonic function, λ is the period of the LST time series, the seasonal component is fitted by several second-order harmonic functions, and the trend component of LST is simulated by m+1 linear functions, then:

[0084] LST t =T t +S t +e t ,t=1,…,n;

[0085]

[0086] Optionally, the conditions for temporally and spatially matching the trend component mutation of LST with the forest loss event include: the forest loss occurs within the confidence range of the trend component mutation of LST detected by the change detection method, and the time difference between the forest loss and the trend component mutation of LST does not exceed a set number of years; if the detected trend component mutation of LST matches the forest loss event, the forest loss window of the forest loss pixel is defined as the union of the year of the trend component mutation of LST and the year of the forest loss event.

[0087] Optionally, background forest pixels controlled by each forest loss pixel are selected, and the background forest pixels meet the following conditions: the background forest pixel is within a search window within a set distance range from the forest loss pixel, the forest coverage change rate of the background forest pixel is less than a set value and is less than the loss percentage of the forest loss pixel, the elevation difference between the background forest pixel and the forest loss pixel is less than a set value, and the number of background forest pixels exceeds a set ratio of all background pixels.

[0088] Optionally, for each forest loss pixel, the LST change caused by forest loss is obtained by calculating the difference between the LST change value observed in the forest loss pixel and the LST change value observed in the background forest pixel during the same period.

[0089] Optionally, let the trend component of LST after forest loss be ΔT, the target forest loss pixel be o, the background stable forest pixel be bg, and the trend component of forest loss pixel after loss be T o,after The trend group of forest loss pixels one year before the loss is T_1 o,before The change in the trend component one year after forest loss is ΔT_1, and the annual scale difference between the trend component of the forest loss pixel after the loss and the trend component of the forest loss pixel one year before the loss is δT o The average difference in trend components of all background forest pixels is δTbg , then the time series change of the trend component of LST after forest loss is expressed as ΔT; the change of the trend component one year after forest loss ΔT_1 represents the immediate impact of forest loss on LST, and the calculation formula is:

[0090] ΔT=δT o -δT bg ;

[0091] δT o =T o,after -T_1 o,before .

[0092] Optionally, let the linear slope of the trend component of the forest loss pixel LST before loss be T_slope o,before The linear slope of the trend component of the forest loss pixel LST after the loss is T_slope o,after The linear slope of the trend component of the forest loss pixel LST changes from before to after the loss as δT_slope o , the linear slope from the loss to the next mutation or the end of the time series is T_slope o,after The linear slope of the trend component of the background forest pixel LST before the loss is T_slope bg,before , the linear slope of the trend component of the background forest pixel LST after loss is T_slope bg,after The difference between the linear slope of the trend component of the background forest pixel LST before and after the loss is δT_slope bg , the forest loss pixel has the same climate signal as the background forest pixel before the loss, that is, T_slope o,before =T_slope bg,before , the linear slope change of the trend component of LST after the loss is ΔT_slope, and the gradual change of the trend component of LST is quantified by the linear slope change of the trend component of LST after the loss, that is:

[0093] ΔT_slope=δT_slope o -δT_slope bg ;

[0094] δT_slope o =T_slope o,after -T_slope o,before .

[0095] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method to quantify the temporal dynamics of land surface temperature response to forest loss, characterized in that include: Extract trend components and seasonal change components from LST time series, and detect sudden changes in trend components; The forest loss pixels that triggered the trend component mutation of LST were selected to evaluate the impact of forest loss on LST, the trend component mutation of LST was matched with the forest loss event in time and space, and the pairing time before and after the loss and the long-term dynamic monitoring period were specified for each forest loss event; The method of combining space and time was adopted to select forest loss pixels and background forest pixels of forest loss pixels, and calculate the sudden change characteristic value and gradual change characteristic value of the trend component of LST; The mutation characteristic value and gradual characteristic value of the trend component of LST are used to quantify the long-term response of LST to forest loss.

2. A method for quantifying the temporal dynamics of surface temperature response to forest loss according to claim 1, characterized in that: The long-term trend in the LST time series is adopted by the piecewise linear function, and multiple second-order harmonic functions are used to simulate the seasonal changes in the LST time series. Let the LST of the time series be LST t , the trend component is T t , the seasonal group is S t , the residual is e t , the LST of the time series is the sum of the trend component, the seasonal component and the residual part, t is the time, n is the number of LST observations, t=1,...,n, T t i is a linear function simulating the trend component of LST, is the second-order harmonic function of the seasonal component, m is the number of breakpoints, and the trend component of LST is composed of m+1 linear functions T t i Simulation, i is the number of segments of the linear function, i = 1, ..., m + 1, a i is the slope of the linear function of the i-th segment, b i is the intercept of the linear function of the i-th segment. The LST seasonal component consists of multiple second-order harmonic functions fit, q+1 is the number of harmonic functions, is the seasonal breakpoint, and the amplitude of the jth harmonic function is The phase of the jth harmonic function is tan -1 (σ j,h / γ j,h ), γ j,h and σ j,h is the function that determines the amplitude of the jth harmonic function and phase (tan -1 (σ j,h / γ j,h ), h is the order of the harmonic function, λ is the period of the LST time series, the seasonal component is fitted by several second-order harmonic functions, and the trend component of LST is simulated by m+1 linear functions, then: LST t =T t +S t +e t ,t=1,…,n; 3. A method for quantifying the temporal dynamics of surface temperature response to forest loss according to claim 1, characterized in that: The conditions for temporally and spatially matching the trend component mutation of LST with forest loss events include: the forest loss occurs within the confidence range of the trend component mutation of LST detected by the change detection method, and the time difference between the forest loss and the trend component mutation of LST does not exceed the set number of years; if the detected trend component mutation of LST matches the forest loss event, the forest loss window of the forest loss pixel is defined as the union of the trend component mutation year of LST and the forest loss event year.

4. A method for quantifying the temporal dynamics of surface temperature response to forest loss according to claim 1, characterized in that: Background forest pixels controlled by each forest loss pixel are selected, and the background forest pixels meet the following conditions: the background forest pixel is located within the search window within the set distance range from the forest loss pixel, the forest cover change rate of the background forest pixel is less than the set value and is less than the loss percentage of the forest loss pixel, the elevation difference between the background forest pixel and the forest loss pixel is less than the set value, and the number of background forest pixels exceeds the set ratio of all background pixels.

5. A method for quantifying the temporal dynamics of surface temperature response to forest loss according to claim 1, characterized in that: For each forest loss pixel, the LST change caused by forest loss was obtained by calculating the difference between the LST change value observed in the forest loss pixel and the LST change value observed in the background forest pixel during the same period.

6. A method for quantifying the temporal dynamics of surface temperature response to forest loss according to claim 5, characterized in that: Assume that the trend component of LST after forest loss is ΔT, the target forest loss pixel is o, the background stable forest pixel is bg, and the trend component of the forest loss pixel after loss is T o,after The trend group of forest loss pixels one year before the loss is T1 o ,before The change in the trend component one year after forest loss is ΔT_1, and the annual scale difference between the trend component of the forest loss pixel after the loss and the trend component of the forest loss pixel one year before the loss is δT o The average difference in trend components of all background forest pixels is δT bg , then the time series change of the trend component of LST after forest loss is expressed as ΔT; the change of the trend component one year after forest loss ΔT_1 represents the immediate impact of forest loss on LST, and the calculation formula is: ΔT=δT o -δT bg ; δT o =T o,after -T_1 o,before 。 7. A method for quantifying the temporal dynamics of surface temperature response to forest loss according to claim 5, characterized in that: Assume that the linear slope of the trend component of the forest loss pixel LST before the loss is Tslope o,before The linear slope of the trend component of the forest loss pixel LST after the loss is Tslope o,after The linear slope of the trend component of the forest loss pixel LST before and after the loss is δTslope o , the linear slope from the loss to the next mutation or the end of the time series is Tslope o,after The linear slope of the trend component of the LST of the background forest pixel before the loss is Tslope bg,before , the linear slope of the trend component of the background forest pixel LST after loss is Tslope bg,after The difference between the linear slope of the trend component of the background forest pixel LST before and after the loss is δTslope bg , the forest loss pixel has the same climate signal as the background forest pixel before the loss, that is, Tslope o,before =Tslope bg,before , the linear slope change of the trend component of LST after the loss is ΔTslope, and the gradual change of the trend component of LST is quantified by the linear slope change of the trend component of LST after the loss, that is: ΔTslope=δTslope o -δTslope bg ; δTslope o =Tslope o,after -Tslope o,before 。 8. A system for quantifying the temporal dynamics of land surface temperature response to forest loss, characterized in that: It includes an extraction and detection unit, a matching unit, a selection and analysis unit, and a processing unit; Extraction and detection unit, used to extract trend components and seasonal change components in the LST time series, and detect sudden changes in trend components; The matching unit is used to select forest loss pixels that trigger the trend component mutation of LST to evaluate the impact of forest loss on LST, match the trend component mutation of LST with the forest loss event in time and space, and specify the pairing time before and after the loss and the long-term dynamic monitoring period for each forest loss event; Selection and analysis units are used to select forest loss pixels and background forest pixels of forest loss pixels by using a method combining space and time, and calculate the sudden change characteristic value and gradual change characteristic value of the trend component of LST; The processing unit is used to quantify the long-term response of LST to forest loss using the mutation characteristic value of the trend component of LST and the gradual characteristic value of the trend component.

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