CN111460386A - Spatial diversity detection method for effective time of regional ecological construction - Google Patents
Spatial diversity detection method for effective time of regional ecological construction Download PDFInfo
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Abstract
The invention provides a spatial differentiation detection method for effective time of regional ecological construction, which comprises the following steps: s1, acquiring vegetation index data sets of the ecological construction project of the area to be detected for years before and after the ecological construction project is executed; step S2, dividing the area to be detected into macroscopic units, and determining the change rate change threshold of the vegetation index fitting line in each unit; step S3, determining time period threshold values of all macro units; step S4, judging whether the vegetation index/annual map of each basic unit has obvious turning time; and step S5, judging whether the time point is the obvious effective time for ecological construction according to the change rate of the turning time point and the fitted lines on the two sides of the turning time point. The vegetation index/annual graph is drawn based on the vegetation index data, and the vegetation index fluctuation condition in the vegetation index/annual graph is calculated through the set fitted line change rate change threshold and the set time period threshold, so that the initial years of ecological restoration and the ecological restoration rate in different areas can be effectively determined.
Description
Technical Field
The invention belongs to the technical field of ecological construction, and particularly relates to a spatial diversity detection method for effective time of regional ecological construction.
Background
Land degradation and desertification are important environmental and socioeconomic problems facing the world, and China is one of the most seriously affected areas, especially the agricultural and pastoral staggered zones and the surrounding areas in China. In order to solve the problems of land degradation and desertification, China implements a plurality of large-scale ecological construction projects, such as protection forest system construction projects of key areas (three north protection forests, middle and upper protection forests of Yangtze river, coastal protection forests and the like), returning farming (grass) projects, wild animal and plant protection and natural protection area construction projects of the whole country, natural forest protection projects, desertification comprehensive treatment projects and the like. The development of the evaluation of the implementation effect of the ecological construction engineering is beneficial to determining the difference of the implementation effect of the engineering, and provides an important reference basis for identifying the implementation case of the engineering, perfecting the construction measures of the engineering and popularizing the implementation experience of the engineering.
The current ecological construction effect is reflected by the land utilization change and vegetation coverage increase and decrease before and after ecological construction is implemented. In the aspect of land utilization change, the ecological construction effect is evaluated mainly by analyzing the change characteristics of the data of the current land utilization situation at the beginning and the end of the evaluation period, the evaluation precision mainly depends on the subdivision degree of the land utilization type, the change of the unchanged ecological function of the land utilization type is difficult to effectively reflect, and the continuous change of the ecological construction effect is difficult to effectively reflect due to the long acquisition period of the data of the current land utilization situation. And in the aspect of vegetation coverage increase and decrease, the ecological construction effect is evaluated mainly by performing trend inspection or linear fitting on the continuous change of the annual vegetation index in the evaluation period, and the continuity of the ecological construction effect is enhanced compared with the land utilization change. The evaluation periods in the two aspects are determined according to the execution time of the ecological construction project, however, in the specific execution process of the ecological construction project, different regions have certain time sequence arrangement, the project effective time also has certain hysteresis, the ecological recovery rate in some regions is underestimated, and the actual effective years of the ecological construction project in different regions are difficult to obtain effectively.
Disclosure of Invention
The invention provides a method for detecting the spatial diversity of the ecological construction effective time in an implementation area based on the vegetation index data of the long time sequence of the ecological construction engineering implementation area, which can effectively determine the ecological restoration starting years in different areas and respectively calculate the ecological restoration rate from the ecological restoration starting years.
Specifically, the invention provides a spatial differentiation detection method of effective time for regional ecological construction, which is characterized by comprising the following steps of:
s1, acquiring a vegetation index annual maximum synthetic data set of the ecological construction project of the area to be detected for years before and after the ecological construction project is executed;
step S2, dividing the area to be detected into a limited number of macro units, generating a vegetation index/year graph of each macro unit, and determining a change rate change threshold of a fitting line of the area to be detected according to the change rate of the fitting line of the vegetation index generated in each macro unit;
step S3, setting a time period threshold value according to the vegetation index/year graph of each macro unit;
step S4, judging whether the vegetation index/annual graph of the basic unit in the macro unit has an obvious turning time point according to the set vegetation index fitting line change rate change threshold and the time period threshold;
and step S5, judging whether the time point is the obvious effective time of ecological construction according to the change rate of the turning time point and the fitted lines on the two sides of the turning time point, and respectively calculating the ecological recovery rate from the obvious effective time.
Further, in step S2, the method further includes the steps of:
step S21, dividing the area to be detected into macro units according to administrative areas or natural areas, and calculating the average value of the vegetation index values of the pixel units in each year within the range of each macro unit in the synthetic data set;
step S22, generating the vegetation index/year map of each macro unit according to the year of the vegetation index of the area to be detected;
step S23, turning points are set according to the execution period of the ecological construction project, the curve in the vegetation index/annual map is divided into 2 sections, and a linear regression fitting line of each section of the curve is generated;
and step S24, calculating the change rate of the fitted line according to the change rate of the two sections of fitted lines, and setting a change rate change threshold.
Further, in step S24, the change rate of the fit line is the difference of the change rates of the two pieces of fit line; and selecting the minimum value in the change rate change of the fit line, and setting the value of 50-70% of the minimum value as the change rate change threshold value.
Further, in step S4, the method further includes the steps of:
step S41, 2 turning time points with any position in the vegetation index/annual map of each basic unit are set in sequence, 3 segments of fitting lines of all conditions are generated, and a group of data with the minimum sum of squares of estimation errors is selected from the fitting lines; judging whether each time period in 3 time periods consisting of 2 turning time points in the group of data is larger than the time period threshold value, if so, executing step S42, and if not, executing step S43;
step S42, determining whether the change rate of the vegetation index fit line formed from the beginning year to the first turning time point and the first turning time point to the second turning time point, and from the first turning time point to the second turning time point and the second turning time point to the ending year is greater than the change rate change threshold, if yes, determining that the first turning time point and the second turning time point are the turning time points of the vegetation index/year map of the basic unit, and executing step S5, if no, executing step S43;
step S43, setting 2 turning time points with any position in the vegetation index/annual map of the basic unit, generating 3 fitting lines of all conditions, and selecting a group of data with the minimum sum of the square of the estimation error and the goodness-of-fit ratio from the fitting lines; judging whether each time period in 3 time periods consisting of 2 turning time points in the group of data is larger than the time period threshold value, if not, executing step S44, if so, executing step S42, and if not, executing step S42, executing step S44;
step S44, excluding 2 turning time points in the vegetation index/annual map of the basic unit, setting 1 turning time point at any position in the vegetation index/annual map of the basic unit, generating 2 fitting lines in all cases, and selecting a group of data with the minimum sum of squares of estimation errors; judging whether each time period in 2 time periods consisting of 1 turning time point in the group of data is larger than the time period threshold value, if so, executing step S45, and if not, executing step S46;
step S45, judging whether the change rate of the fitted line formed by the linear functions at the two sides of the turning time point is larger than the change rate change threshold, if so, determining that the turning time point is the turning time point of the vegetation index/annual map of the basic unit, and executing step S5, if not, executing step S46;
at step S46, there is no significant turning time in the vegetation index/year map of the basic cell.
Further, in step S4, the basic unit is an area represented by a pixel point in the macro unit.
The invention has the beneficial effects that:
the invention provides a vegetation index data-based ecological construction method, which comprises the steps of drawing a vegetation index/year map according to the implementation range and time of ecological construction engineering, generating a fitting line through the vegetation index/year, and setting a threshold value according to the change rate of the vegetation index; and calculating the vegetation index fluctuation condition in the vegetation index/annual map through a set threshold value to obtain an optimal group of data. Through the calculated data, the initial years of ecological restoration in different areas can be effectively determined, and ecological restoration rates are calculated from the initial years of ecological restoration respectively.
The invention adopts an exhaustive mode to calculate all the conditions in the vegetation index/annual graph, and can more accurately screen the years with obvious changes of the vegetation index by taking the minimum value of the sum of the squares of the estimation errors of the fit line and the sum of the ratio of the squares of the estimation errors to the goodness of fit as the screening condition.
Drawings
Fig. 1 is a schematic flow chart of a method for detecting spatial differentiation of time taken for regional ecological construction according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a vegetation index fit line change rate of a high tableland gully region in engineering implementation time in the method for detecting spatial differentiation of regional ecological construction effect time according to the embodiment of the present invention;
fig. 3 is a schematic diagram of a change rate of a vegetation index fit line of a valley plain area at the engineering implementation time in the method for detecting spatial differentiation of effective time of regional ecological construction provided by the embodiment of the present invention;
fig. 4 is a schematic view of a vegetation index fit line of a high tableland gully region in project effective time in the method for detecting spatial differentiation of regional ecological construction effective time according to the embodiment of the present invention;
fig. 5 is a schematic diagram of a vegetation index fitting line of a valley plain area in engineering effective time in the method for detecting spatial differentiation of effective time of regional ecological construction provided by the embodiment of the present invention;
fig. 6 is a schematic view of a main turning point year of a vegetation index based on pixel points in a return to cultivation (grass) project in a loess plateau area in a spatial differentiation detection method of an effective time for regional ecological construction according to an embodiment of the present invention.
Fig. 7 is a schematic diagram of a characteristic of a turning point of a vegetation index based on a pixel point in a return to cultivation (grass) project in a loess plateau area in a spatial differentiation detection method of an effective time for regional ecological construction according to an embodiment of the present invention.
Fig. 8 is a schematic view of an ecological restoration rate calculated from the beginning of ecology by pixel points in the return to agriculture (grass) project in a loess plateau area in the method for detecting spatial differentiation of effective time of regional ecological construction according to the embodiment of the present invention.
Detailed Description
The technical solution of the present invention is further described in detail by the following embodiments with reference to fig. 1 to 8.
As shown in fig. 1, the present invention provides a method for detecting spatial diversity of regional ecological construction effective time, which is based on vegetation index data of a long time sequence in an ecological construction engineering implementation area, detects spatial diversity of ecological construction effective time in the implementation area, and calculates ecological restoration rate from the year of ecological restoration, and the method comprises the following steps:
s1, acquiring a vegetation index annual maximum synthetic data set of the ecological construction project of the area to be detected for years before and after the ecological construction project is executed;
step S2, dividing the area to be detected into a limited number of macro units, generating a vegetation index/year graph of each macro unit, and determining a change rate change threshold of a fitting line of the area to be detected according to the change rate of the fitting line of the vegetation index generated in each macro unit;
step S3, setting a time period threshold value according to the vegetation index/year graph of each macro unit;
step S4, judging whether the vegetation index/annual graph of the basic unit in the macro unit has an obvious turning time point according to the set vegetation index fitting line change rate change threshold and the time period threshold;
and step S5, judging whether the time point is the obvious effective time of the ecological construction according to the turning time point and the change rate of the fit line at the two sides of the turning time point, and calculating the ecological recovery rate from the obvious effective time.
Specifically, in step S1, determining the year range to be detected according to the execution period of the ecological construction project; acquiring original vegetation index data of the area to be detected within a year range; the vegetation index raw data is GIMMS AVHRR NDVI data, the spatial resolution is 8km, and the time resolution is 15 days. And processing the original vegetation index data according to the year, screening out the maximum value of each pixel point in the data of each year in the area to be detected, and generating a vegetation index annual maximum value synthetic data set of each year in the area to be detected.
The step S2 further includes the steps of:
step S21, dividing the area to be detected into macro units according to administrative areas or natural areas, and calculating the average value of the vegetation index values of the pixel units in each year within the range of each macro unit in the synthetic data set;
step S22, generating a vegetation index/year map of each macro unit according to the year of the vegetation index of the area to be detected;
step S23, setting turning points according to the execution period of the ecological construction project, dividing the vegetation index/year curve into 2 sections, and generating a linear regression fitting line for each section of curve;
generating a fitted line of Y (a + bX) based on a linear regression mode calculated by a least square method, wherein b is the change rate of the fitted line;
and step S24, solving the change rate change of the fitting line according to the change rate of the two fitting lines in each macro unit, and setting a change rate change threshold.
Respectively obtaining the change rate of a front fit line and a rear fit line in an execution period in each macro unit, and obtaining the change rate change of the fit lines by subtracting the change rate of the rear fit line and the change rate of the front fit line; and selecting the minimum value in the change rate change of the fitting line of each macro unit, and setting the value of 50-70% of the minimum value as a change rate change threshold value.
In step S3, a time period threshold is set according to the vegetation index/year map of each macro cell of the ecological construction project of the area to be detected. The vegetation index sequence is divided by the turn time into 2-3 time segments, and a minimum annual threshold is determined for each time segment, typically 3-4 years.
In step S4, the method further includes:
step S41, 2 turning time points with any position in the vegetation index/year graph of the basic units in each macro unit are set in sequence, 3 segments of fitting lines of all conditions are generated, and an optimal group of data is selected from the fitting lines; the area of any pixel point in the macro unit is a basic unit. It is determined whether each of the 3 time periods consisting of the 2 turning time points in the set of data is greater than the time period threshold determined in step S3, if so, step S42 is performed, and if not, step S43 is performed.
Specifically, each basic unit is set in sequenceThe vegetation index/year graph has 2 turning time points, 3 fitting lines are generated according to the 2 turning time points, and the estimation error square sigma of the 3 fitting lines is obtained2Summing, wherein the estimation error σ is a difference between the vegetation index value for any year and the vegetation index value estimated on the fitted line; summarizing and calculating the estimation error square sigma of the 3 segments of fit lines under the condition that all 2 turning time points are respectively set in each year2Summing, selecting the square sigma of the estimated error of 3 linear functions2The value of the sum is the smallest set of data; judging whether each of 3 time periods consisting of 2 turning time points in the group of data is greater than the time period threshold determined in step S3, if so, executing step S42, and if not, executing step S43;
step S42, determining whether the change of the rate of change of the fit line formed from the starting year to the first turning time point and the first turning time point to the second turning time point, and from the first turning time point to the second turning time point and the second turning time point to the ending year is greater than the change rate change threshold determined in step S2, if yes, determining that the first turning time point and the second turning time point are obvious turning times of the vegetation index of the basic unit, and executing step S5, if no, executing step S43.
Step S43, setting 2 turning time points at any position in the vegetation index/annual map of the basic unit, generating 3 fitting lines of all conditions, and selecting an optimal group of data; it is determined whether each of the 3 time periods consisting of the 2 turning time points in the set of data is greater than the time period threshold determined in step S3, if not, step S44 is performed, if yes, step S42 is performed, and if no, step S42 is performed, step S44 is performed.
Specifically, 2 turning time points are set in a vegetation index/year graph of the basic unit, 3 fitting lines are generated according to the 2 turning time points, and the ratio of the square of the estimation error of all the years of the 3 fitting lines to the goodness of fit is obtained
Wherein the fit is excellentDegree R2A ratio of a sum of squares of differences between the vegetation index value and the actual vegetation index mean on the fitted line to a sum of squares of differences between the actual vegetation index value and the actual vegetation index mean; summarizing and calculating the ratio of the square of the estimated error to the goodness of fit of 3 segments of fit lines under the condition that all 2 turning time points are respectively set in each year
Summing, selecting the ratio of the square of the estimated error to the goodness of fit of 3 sections of linear functions
A set of data having the smallest sum; judging whether each time period in 3 time periods consisting of 2 turning time points in the group of data is larger than the time period threshold determined in the step S3, if not, executing the step S44, if so, executing the step S42, and if not, executing the step S44 when executing the step S42;
step S44, excluding 2 turning time points in the vegetation index/annual map of the basic unit, setting 1 turning time point at any position in the vegetation index/annual map of the basic unit, generating 2 segments of fitting lines in all conditions, and selecting an optimal group of data; it is determined whether each of the 2 time periods consisting of 1 turning time point in the set of data is greater than the time period threshold determined in step S3, if so, step S45 is performed, and if not, step S46 is performed.
Specifically, 2 turning time points in the vegetation index/annual map of the basic unit in the area to be detected are excluded, and only 1 turning time point exists in the vegetation index/annual map of the basic unit; generating 2 segments of fitting lines according to the 1 turning time point, and solving the square error sigma of the 2 segments of fitting lines2Summing; summarizing and calculating the estimation error square sigma of 2 segments of fit lines under the condition that all 1 turning time points are respectively set in each year2Summing, selecting the square sigma of the estimated error of 2 linear functions2The value of the sum is the smallest set of data; judging each time segment of 2 time segments formed by 1 turning time point in the group dataWhether both are greater than the time period threshold determined in step S3, if yes, perform step S45, if no, perform step S46;
step S45, judging whether the change rate of the fitted line formed by the linear functions at the two sides of the turning time point is larger than the change rate change threshold determined in step S2, if so, determining that the turning time point is the obvious turning time in the vegetation index/annual map of the basic unit, and executing step S5, otherwise, executing step S46;
at step S46, there is no significant turning time in the vegetation index/year map of the basic cell.
Step S5, obtaining the turning time point and the variation rate of the fitted lines at the two sides of the turning time point, judging whether the variation rate of the fitted lines at the two sides of the turning time point is larger than 0, if so, the turning time point is the starting time of the vegetation index obvious increase, namely the obvious effective time of the ecological construction project, and calculating the ecological restoration rate from the obvious effective time, if not, the turning time point is the starting time of the vegetation index obvious reduction.
In one embodiment, the loess plateau area is selected as the case area, and the natural area is used as the macro unit to show the process of determining the turning time of the finer pixel unit. Loess plateau areas are one of the most serious areas of China and even the global water and soil loss, the returning from cultivation to forest (grass) engineering is realized since 1999, and the ecological construction has more remarkable effect. The method comprises the following steps of:
step S1, preparing a vegetation index annual maximum value synthetic data set about 10 years before and after the loess plateau region returning forest (grass) project is implemented, wherein the data is AVHRR GIMMS NDVI data in 1990-2013.
As shown in fig. 2-3, in step S2, the natural region of the loess plateau region is divided into a limited number of macro units, and an average value of the vegetation indexes of the pixel units in each year of 1990-2013 is calculated by taking two macro units, namely a high-ravine region and a valley plain region as an example; generating a vegetation index/year map of each natural area unit in the loess plateau area; determining the execution period of the ecological construction project according to the starting implementation time of the returned agricultural (grass) project, and determining the starting time of the ecological construction effect evaluation to be 2000 years; taking 2000 years as turning points, dividing the vegetation index/year curve into 2 segments, and respectively generating a fitting line of the 2 segments of the curve.
Calculating the change rate of a vegetation index fitting line before and after 2000 years of each natural area unit of the loess plateau in a linear regression mode based on least square calculation; comparing the change rate of vegetation index fitting lines before and after 2000 years of different natural area units, and determining that the minimum threshold value of the change of the vegetation index increase rate is 0.002yr-1。
Step S3, setting time period thresholds of two macro units in the high tableland gully area and the valley plain area in this embodiment, and setting the threshold of each time period to be 3 years.
As shown in fig. 4-5, in step S4, it is determined whether the vegetation indexes in the units of the high tableland gully region and the valley plain region in the loess plateau have obvious turning time points;
in this embodiment, only 2005, the highland gully region is the obvious turning time in the vegetation index/year map, and is the obvious time to start to take effect in the returning to farming (grass) project; in 1993 and 2005, the valley plain area is the obvious turning time in a vegetation index/year graph, and in 2005, the obvious time for the return to agricultural (grass) engineering to start taking effect.
As shown in fig. 6, based on the variation rate threshold and the time threshold of the fitted line set in the steps S1 to S3, in step S4, the vegetation index turning time is determined by using each pixel point in the loess plateau region as a basic unit, and each pixel unit represents 8km × 8km space. And counting all turning time areas of the vegetation index, respectively drawing pixel points corresponding to 4 turning times with larger areas on a loess plateau map, and showing the positions of pixel units with more turning time years of the vegetation index in the area to be detected.
As shown in fig. 7, based on the turning time of the loess plateau based on the vegetation index of the pixel unit obtained in the above steps S1 to S4, the change rate of the fit line at both sides of the turning time point is calculated by using the pixel point as the basic unit according to step S5, and the time for starting the return forest (grass) engineering to take effect is determined, and the starting time when the vegetation index is obviously increased is the time for starting the return forest (grass) engineering to take effect. And respectively drawing pixel units corresponding to the turning time of the vegetation indexes which are obviously increased and reduced on a loess plateau map, and showing the obvious effective time of the ecological construction engineering in the area to be detected.
As shown in fig. 8, on the basis of the significant effect time of the ecological engineering of the loess plateau based on the pixel units obtained in the above steps S1 to S5, the vegetation index increase rate is calculated from the effect start time by using the pixel points as the basic units according to step S5, the vegetation index increase rate is assigned to the pixel units corresponding to the effect time and is drawn on the map of the loess plateau, and the ecological restoration rate in the detection area is displayed.
Although the present invention has been described in terms of the preferred embodiment, it is not intended that the invention be limited to the embodiment. Any equivalent changes or modifications made without departing from the spirit and scope of the present invention also belong to the protection scope of the present invention. The scope of the invention should therefore be determined with reference to the appended claims.
Claims (5)
1. A spatial diversity detection method for effective time of regional ecological construction is characterized by comprising the following steps:
s1, acquiring a vegetation index annual maximum synthetic data set of the ecological construction project of the area to be detected for years before and after the ecological construction project is executed;
step S2, dividing the area to be detected into a limited number of macro units, generating a vegetation index/year graph of each macro unit, and determining a change rate change threshold of a fitting line of the area to be detected according to the change rate of the fitting line of the vegetation index generated in each macro unit;
step S3, setting a time period threshold value according to the vegetation index/year graph of each macro unit;
step S4, judging whether the vegetation index/annual graph of the basic unit in the macro unit has an obvious turning time point according to the set vegetation index fitting line change rate change threshold and the time period threshold;
and step S5, judging whether the time point is the obvious effective time of ecological construction according to the change rate of the fitting line at the turning time point and the two sides of the turning time point, and calculating the ecological recovery rate from the obvious effective time.
2. The time-of-effect spatial differentiation detection method according to claim 1, characterized by further comprising, in step S2, the steps of:
step S21, dividing the area to be detected into macro units according to administrative areas or natural areas, and calculating the average value of the vegetation index values of the pixel units in each year within the range of each macro unit in the synthetic data set;
step S22, generating the vegetation index/year map of each macro unit according to the year of the vegetation index of the area to be detected;
step S23, turning points are set according to the execution period of the ecological construction project, the curve in the vegetation index/annual map is divided into 2 sections, and a linear regression fitting line of each section of the curve is generated;
and step S24, calculating the change rate of the fitted line according to the change rate of the two sections of fitted lines, and setting a change rate change threshold.
3. The time-of-effect spatial differentiation detection method according to claim 2, characterized in that in step S24, the fitted line change rate is a difference of the change rates of the two fitted lines; and selecting the minimum value in the change rate change of the fit line, and setting the value of 50-70% of the minimum value as the change rate change threshold value.
4. The time-of-effect spatial differentiation detection method according to claim 1, characterized by further comprising, in step S4, the steps of:
step S41, 2 turning time points with any position in the vegetation index/annual map of each basic unit are set in sequence, 3 segments of fitting lines of all conditions are generated, and a group of data with the minimum sum of squares of estimation errors is selected from the fitting lines; judging whether each time period in 3 time periods consisting of 2 turning time points in the group of data is larger than the time period threshold value, if so, executing step S42, and if not, executing step S43;
step S42, determining whether the change rate of the vegetation index fit line formed from the beginning year to the first turning time point and the first turning time point to the second turning time point, and from the first turning time point to the second turning time point and the second turning time point to the ending year is greater than the change rate change threshold, if yes, determining that the first turning time point and the second turning time point are the turning time points of the vegetation index/year map of the basic unit, and executing step S5, if no, executing step S43;
step S43, setting 2 turning time points with any position in the vegetation index/annual map of the basic unit, generating 3 fitting lines of all conditions, and selecting a group of data with the minimum sum of the square of the estimation error and the goodness-of-fit ratio from the fitting lines; judging whether each time period in 3 time periods consisting of 2 turning time points in the group of data is larger than the time period threshold value, if not, executing step S44, if so, executing step S42, and if not, executing step S42, executing step S44;
step S44, excluding 2 turning time points in the vegetation index/annual map of the basic unit, setting 1 turning time point at any position in the vegetation index/annual map of the basic unit, generating 2 fitting lines in all cases, and selecting a group of data with the minimum sum of squares of estimation errors; judging whether each time period in 2 time periods consisting of 1 turning time point in the group of data is larger than the time period threshold value, if so, executing step S45, and if not, executing step S46;
step S45, judging whether the change rate of the fitted line formed by the linear functions at the two sides of the turning time point is larger than the change rate change threshold, if so, determining that the turning time point is the turning time point of the vegetation index/annual map of the basic unit, and executing step S5, if not, executing step S46;
at step S46, there is no significant turning time in the vegetation index/year map of the basic cell.
5. The method for detecting spatial differentiation of elapsed time according to claim 1, wherein in step S4, said basic unit is an area represented by a pixel point in said macro unit.
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