CN113065464B - Forest remote sensing information change probability model - Google Patents
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Abstract
The invention discloses a forest remote sensing information change probability model, which is described by utilizing a GVI difference image histogram, wherein the establishment of the GVI difference image histogram comprises the following steps: firstly, performing accurate registration of two-stage TM images; establishing a mask image of a forest region; then mask operation is carried out on GVI images; obtaining forest region images, and then respectively obtaining GVI images; if the difference between the weathers of the two images is large, the weathers normalization processing is needed, otherwise, the calculation of the forest variation probability is directly carried out, and the probability image is segmented according to a preset threshold value. The forest remote sensing information change probability model can describe the change probability of the forest by using normal distribution. Along with the implementation of six projects of forestry in China, the serious degradation phenomenon of the forest is reduced, and the condition that a changed area occupies a small part in the whole scenery image is met, so that GVI difference values of the forest are ensured to obey a normal distribution rule.
Description
Technical Field
The invention relates to the technical field of forest remote sensing, in particular to a forest remote sensing information change probability model.
Background
The northern forest area of China belongs to the temperate zone, the four seasons are clear, the change of the physical condition is large, and the change of fallen leaves and broad leaves is particularly obvious. The growing season of deciduous broad-leaved trees is the time interval from the beginning of growth to the end of growth to before dormancy. Because the growth seasons of the forest areas are greatly different, the growth seasons specific to a certain place can be determined according to plant weather and remote sensing data. Although the annual changes in climate are not completely consistent due to global warming and some contingencies, we still assume that the years-averaged climate period is stable.
The remote sensing data for extracting forest variations must be obtained during the growing season. However, in actual production, even if the data obtained in the same growing season are the same, imaging in the same climatic period is difficult to ensure, so that the data difference in different climatic periods is still large, and therefore, normalization processing of the two climates is needed, and the areas with small changes are pressed, so that the areas with large changes are highlighted.
The most immediate result of forest variation is a change in the forest Leaf Area Index (LAI), which drops dramatically when forests degrade. While LAI does not directly correspond to a band of the multi-band remote sensing image. This is because any one band of the TM telemetry image is not a function of the reflected radiance of a single pair of vegetation leaves in that band, but rather a complex non-lambertian system of vegetation-soil that is a multiple function in that band. The Vegetation Index (VI) integrating the data of each wave band is a monotonic function of the LAI, and is more stable and reliable than a single wave band value.
The ideal vegetation index should not only fully reflect vegetation information, but also avoid the influence of soil background. The green cover index (greenness vegetation index) GVI, which is the vertical vegetation index in a multidimensional system, is also the second principal component value in K-T transformation (thysancap transformation) of the multidimensional reflectivity factor of the vegetation-soil system, is characterized by small dispersion in the experimental relationship of LAI, while the saturated LAI value is high (6-8) and is affected little by the soil background.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a forest remote sensing information change probability model, which is described by utilizing a GVI difference image histogram, and the establishment of the GVI difference image histogram comprises the following steps:
firstly, accurately registering two-stage TM images;
step two, establishing a mask image of a forest region;
thirdly, performing mask operation on the GVI image;
step four, obtaining forest region images, and then respectively obtaining GVI images;
and fifthly, if the difference between the weathers of the two images is large, carrying out weathers normalization processing, otherwise, directly calculating the forest variation probability, and dividing the probability image according to a preset threshold value.
Preferably, the mean value of two populations in the invariant in the hypothesis change is respectivelyAnd->The mean value of the whole image is +.>And the proportion of the two populations in the whole image is f and 1-f respectively, according to the statistical theory, the average value of normal distribution can be proved to be a one-dimensional variable, and the two populations can be linearly combined to obtain the following formula:
is obtained by the conversion of the formula (1):
for images of different climates, the mean of the two populations is different, so it is assumed that the mean of the two populations in the images of the two climates, and the mean of the entire image, are respectivelyFrom formula (2):
comparing formula (3) with formula (4), obtaining:
i.e.
For two determined climatic periods in a certain determined regionIs determined, so->Is an invariant independent of the two overall ratios f, although exactly master +.>Difficult but can be considered +.>Is the peak point of the image histogram, and the mean value of the whole image +.>Can be precisely obtained, so canRegarding as the invariant of the GVI images of different climates, which is defined as the distance ratio T of the peak point to the mean point of the histogram of the GVI images of two periods, the climates are normalized to be the mean value of the whole image +.>And T histogram reforming of the image, if the +.>And T value, the GVI image of any two climates in the whole growing season can be normalized.
Preferably, set up: the gray value of the peak value is x 1 The gray average value isAt x 1 The distribution frequency at is H (x 1 ) The distance between the gray value of the peak and the gray mean value is +.>Let the mean of GVI images of another weathered period be +.>The gray value of the peak value is x' 1 Let the variation of the gray mean be +.>The ratio of the gray value of the two-phase peak value to the gray average value distance is T, namely:
if the transformation difference of two populations is not considered, only the change of the two-phase data mean is considered, i.eTwo phases GVIThe histogram transfer function of the image is only that the straight line OA with slope 1 is shifted in y-direction with intercept +.>However, considering the difference of the two populations in the climatic change, although the transformation of the single population is a linear mapping relationship, the combined result of the two population transformations becomes a nonlinear process, which cannot be expressed directly by linear mapping, and therefore a nonlinear mapping function must be selected, a piecewise linear mapping function is selected, and the process of determining the function is divided into two steps: first determining the segment point and the slope of each segment where the gray value x of the peak is selected 1 As the x coordinate of the segment point, the y coordinate value of the segment point is equal to the x coordinate of the point plus the distance change Δx of the peak value relative to the mean value, that is, the distance between the B point and the C point is Δx, thereby determining the slopes of the OB and BA line segments, which are obtained by the formula (6):
if the overall mean change is not considered first, the equation can be transformed into:
the following is obtained:
then:
compared with the prior art, the invention has the following beneficial effects: the forest remote sensing information change probability model can describe the change probability of the forest by using normal distribution. Along with the implementation of six projects of forestry in China, the serious degradation phenomenon of the forest is reduced, and the condition that a changed area occupies a small part in the whole scenery image is met, so that GVI difference values of the forest are ensured to obey a normal distribution rule. Then we define that the probability of a forest variation for a pel is greater than the cumulative probability for all pels whose GVI difference is.
Drawings
FIG. 1 is a flow chart of forest variation information extraction according to the present invention;
FIG. 2 is a histogram A of GVI images of different climates according to the invention;
FIG. 3 is a histogram B of a GVI image of different climates according to the invention;
FIG. 4 is a histogram C of GVI images of different climates according to the invention;
FIG. 5 is a histogram D of GVI images of different climates according to the invention;
FIG. 6 is a schematic diagram of a histogram reformation function in accordance with the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1-6, a forest remote sensing information change probability model is described by using a GVI difference image histogram, and the GVI difference image histogram is established by the following steps:
firstly, accurately registering two-stage TM images;
step two, establishing a mask image of a forest region;
thirdly, performing mask operation on the GVI image;
step four, obtaining forest region images, and then respectively obtaining GVI images;
and fifthly, if the difference between the weathers of the two images is large, carrying out weathers normalization processing, otherwise, directly calculating the forest variation probability, and dividing the probability image according to a preset threshold value.
Preferably, the mean value of two populations in the invariant in the hypothesis change is respectivelyAnd->The mean value of the whole image is +.>And the proportion of the two populations in the whole image is f and 1-f respectively, according to the statistical theory, the average value of normal distribution can be proved to be a one-dimensional variable, and the two populations can be linearly combined to obtain the following formula:
is obtained by the conversion of the formula (1):
for images of different climates, the mean of the two populations is different, so it is assumed that the mean of the two populations in the images of the two climates, and the mean of the entire image, are respectivelyFrom formula (2):
comparing formula (3) with formula (4), obtaining:
i.e.
For two determined climatic periods in a certain determined regionIs determined, so->Is an invariant independent of the two overall ratios f, although exactly master +.>Difficult but can be considered +.>Is the peak point of the image histogram, and the mean value of the whole image +.>Can be precisely obtained, so canRegarding as the invariant of the GVI images of different climates, which is defined as the distance ratio T of the peak point to the mean point of the histogram of the GVI images of two periods, the climates are normalized to be the mean value of the whole image +.>And T histogram reforming of the image, if the +.>And T value, the GVI image of any two climates in the whole growing season can be normalized.
Setting: the gray value of the peak value is x 1 The gray average value isAt x 1 The distribution frequency at is H (x 1 ) The distance between the gray value of the peak and the gray mean value is +.>Let the mean of GVI images of another weathered period be +.>The gray value of the peak value is x 1 ' let the variation of the gray mean be known +.>The ratio of the gray value of the two-phase peak value to the gray average value distance is T, namely:
if the transformation difference of two populations is not considered, only the change of the two-phase data mean is considered, i.eThe histogram transfer function of the two-phase GVI image is shifted only in the y-direction by a straight line OA with slope 1, with intercept +.>However, considering the difference of the two populations in the climatic change, although the transformation of the single population is a linear mapping relationship, the combined result of the two populations becomes a nonlinear process which cannot be expressed directly by linear mapping, and therefore a nonlinear mapping function must be selected, and a piecewise linear mapping function is selectedThe process of determining the function is divided into two steps: first determining the segment point and the slope of each segment where the gray value x of the peak is selected 1 As the x coordinate of the segment point, the y coordinate value of the segment point is equal to the x coordinate of the point plus the distance change Δx of the peak value relative to the mean value, that is, the distance between the B point and the C point is Δx, thereby determining the slopes of the OB and BA line segments, which are obtained by the formula (6):
if the overall mean change is not considered first, the equation can be transformed into:
the following is obtained:
then:
in consideration of the influence of comprehensive errors such as scale, data registration and ground investigation, the accuracy analysis is performed by taking the overlapping of more than 50% of the area of the polygon of the reference data as correct, namely, the erroneous classification is judged when the overlapping area of the known polygon and the monitoring result is less than 50%. Thus, the spatial position overlapping rate (table 1 and table 2) under various post-treatment conditions can be obtained, and the spatial position overlapping rate is used as a monitoring accuracy index.
Table 1 shows statistics and comparison results based on individual pixels
The accuracy of degradation monitoring was 81.0% when no post-treatment of any kind was performed.
Table 2 shows statistics and comparison results after 3*3 template filtration
The accuracy of degradation monitoring was 80.1% when post-treatment of 3*3 templates was performed.
Table 3 shows statistics and comparison results after 4*4 template filtration
The accuracy of degradation monitoring was 79.6% when post-treatment of 4*4 templates was performed.
In short, the post-class processing has little influence on the overall monitoring precision, but in actual production management, a template with proper size is selected according to the forest stand condition to carry out the post-class processing.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (1)
1. The method for establishing the forest remote sensing information change probability model uses GVI difference image histogram to describe the forest remote sensing information change probability model, and is characterized in that: the GVI difference image histogram creation includes the steps of:
firstly, accurately registering two-stage TM images;
step two, performing mask processing on the image obtained in the step one to obtain a forest region image;
thirdly, aiming at the forest region image obtained in the second step, respectively obtaining GVI images;
step four, if the difference between the weathers of the two images is large, carrying out weathers normalization processing, otherwise, directly calculating the forest variation probability, and dividing the probability image according to a preset threshold value;
in the fourth step, it is assumed that the average value of two populations in the invariant in the climate change is respectivelyAnd->The mean value of the whole image is +.>And the proportion of the two populations in the whole image is f and 1-f respectively, according to the statistical theory, the average value of normal distribution can be proved to be a one-dimensional variable, and the two populations can be linearly combined to obtain the following formula:
is obtained by the conversion of the formula (1):
for images of different climates, the mean of the two populations is different, so it is assumed that the mean of the two populations in the images of the two climates, and the mean of the entire image, are respectivelyFrom formula (2):
comparing formula (3) with formula (4), obtaining:
i.e.
For two determined climatic periods in a certain determined regionIs determined, so->Is an invariant independent of the two overall ratios f, although exactly master +.>It is difficult but can be considered as follows based on the assumptionIs the peak point of the image histogram, and the mean value of the whole image +.>Can be precisely obtained, so canRegarding as the invariant of the GVI images of different climates, which is defined as the distance ratio T of the peak point to the mean point of the histogram of the GVI images of two periods, the climates are normalized to be the mean value of the whole image +.>And T histogram reforming of the image, if the +.>And T value, the GVI image of any two climatic periods of the whole growing season can be normalized;
in the fourth step, set: the gray value of the peak value is x 1 The gray average value is x, at x 1 The distribution frequency at is H (x 1 ) The distance between the gray value of the peak value and the gray average value is x-x 1 Let the mean value of GVI image of another weathered period be x ', the gray value of the peak be x' 1 Let the variation of the known gray average beThe ratio of the gray value of the two-phase peak value to the gray average value distance is T, namely:
if the transformation difference of two populations is not considered, only the change of the two-phase data mean is considered, i.eThe histogram transfer function of the two-phase GVI image is shifted only in the y-direction by a straight line OA with slope 1, with intercept +.>However, considering the difference of the two populations in the climatic change, although the transformation of the single population is a linear mapping relationship, the combined result of the two populations becomes a nonlinear process which cannot be expressed directly by linear mapping, and therefore a nonlinear mapping function must be selected, and a piecewise linear mapping is selectedThe process of determining the function is divided into two steps: first determining the segment point and the slope of each segment where the gray value x of the peak is selected 1 As the x coordinate of the segment point, the y coordinate value of the segment point is equal to the x coordinate of the point plus the distance change Δx of the peak value from the mean value, that is, the distance between the B point and the C point is Δx, thereby determining the slopes of the OB and BA line segments, which are obtained by the formula (6):
x-x 1 ＝T(x'-x′ 1 ) (7)
if the overall mean change is not considered first, the equation can be transformed into:
x-x 1 ＝T(x-x′ 1 ) (8)
the following is obtained:
then:
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| CN110135322A (en) * | 2019-05-09 | 2019-08-16 | 航天恒星科技有限公司 | A kind of time series forest change monitoring method based on IFI |
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| CN105046188A (en) * | 2015-04-13 | 2015-11-11 | 中南林业科技大学 | MODIS mixed pixels decomposition forest information extraction method |
| CN104851087A (en) * | 2015-04-17 | 2015-08-19 | 华中农业大学 | Multi-scale forest dynamic change monitoring method |
| CN105718936A (en) * | 2016-02-02 | 2016-06-29 | 福州大学 | Forest dynamic change mode automatic extraction method |
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