CN110175537B - Method and system for evaluating land degradation condition by fusing multi-source remote sensing indexes - Google Patents

Method and system for evaluating land degradation condition by fusing multi-source remote sensing indexes Download PDF

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CN110175537B
CN110175537B CN201910387651.3A CN201910387651A CN110175537B CN 110175537 B CN110175537 B CN 110175537B CN 201910387651 A CN201910387651 A CN 201910387651A CN 110175537 B CN110175537 B CN 110175537B
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杨超
李清泉
邬国锋
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Shenzhen University
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Abstract

The invention discloses a method and a system for evaluating land degradation condition by fusing multi-source remote sensing indexes, wherein the method comprises the following steps: acquiring a remote sensing image and preprocessing the image according to the actual situation of a research area; carrying out land utilization and land coverage classification from the preprocessed remote sensing image, and recoding different land types according to the influence on land degradation from small to large; extracting vegetation coverage, water and soil loss degree, soil moisture content and soil wind erosion degree from the preprocessed remote sensing image; after the image data of the five indexes are subjected to data standardization, superposing the image maps, constructing a comprehensive land degradation index and carrying out function calculation; and outputting a calculation result of the land degradation degree according to the comprehensive land degradation index, and grading the land degradation condition according to the size of the comprehensive land degradation index. The method and the system obtain the comprehensive land degradation index by integrating a plurality of indexes, and realize the rapid identification of the land degradation condition from the remote sensing image.

Description

Method and system for evaluating land degradation condition by fusing multi-source remote sensing indexes
Technical Field
The invention relates to the technical field of remote sensing and ecological environment protection, in particular to a method and a system for comprehensively evaluating land degradation conditions by fusing multi-source remote sensing indexes.
Background
Land resources provide important material conditions for human survival and development. However, in recent years, unreasonable land utilization patterns and poor land resource management, coupled with population expansion, have led to severe global land degradation. Land deterioration refers to the reduction or loss of land biological or economic production capacity caused by human activities, and is influenced by natural processes such as climate change, so that the influence of land deterioration is enlarged.
The remote sensing and Geographic Information System (GIS) technology has achieved good results in the land degradation research, but the current commonly used land degradation indexes are not enough, and the land degradation indexes are single in use, and cannot comprehensively reflect the degree of land degradation and the space-time characteristics. Many researchers have used single or composite indicators to develop studies on land degradation, such as Normalized Differential Vegetation Index (NDVI), water and soil loss (WLSE), land use and coverage changes, and land desertification. Although the above-mentioned indexes can reveal the degree of land deterioration to some extent, the causes of land deterioration are complex, and land deterioration is usually caused by the superposition of various phenomena, including unreasonable land utilization development, water and soil loss, vegetation coverage reduction, drought, wind erosion, and the like.
That is to say, in the prior art, the land degradation condition is evaluated only by a single index or a few indexes, and the degree and the space-time characteristics of land degradation cannot be comprehensively reflected, so that the evaluation data of the land degradation condition in the research area is inaccurate, timely feedback and measures cannot be effectively made according to the land degradation state, and effective support cannot be provided for ecological environment investigation and land degradation disaster reduction.
Accordingly, there is a need for improvements and developments in the art.
Disclosure of Invention
The invention aims to solve the technical problems that in the prior art, the land degradation condition is only evaluated through a single index or a few indexes, the degree and the space-time characteristics of land degradation cannot be comprehensively reflected, so that the evaluation data of the land degradation condition of a research area is inaccurate, and timely feedback and measures cannot be effectively made according to the land degradation state.
The technical scheme adopted by the invention for solving the technical problem is as follows:
a method for evaluating land degradation conditions by fusing multi-source remote sensing indexes comprises the following steps:
acquiring a remote sensing image and carrying out image preprocessing according to the actual situation of a research area;
carrying out land utilization and land coverage classification from the preprocessed remote sensing image, and recoding different land types according to the influence on land degradation from small to large;
extracting vegetation coverage, water and soil loss degree, soil moisture content and soil wind erosion degree from the preprocessed remote sensing image;
carrying out data standardization on the image data of the land utilization and land coverage, the vegetation coverage, the water and soil loss degree, the soil water content and the land wind erosion degree;
superposing the image maps of the land utilization and the land coverage, the vegetation coverage, the water and soil loss degree, the soil water content and the land wind erosion degree, constructing a comprehensive land degradation index and carrying out function calculation;
and outputting a calculation result of the land degradation degree according to the comprehensive land degradation index, and grading the land degradation condition according to the size of the comprehensive land degradation index.
The method for evaluating the land degradation condition by fusing the multi-source remote sensing indexes comprises the following steps of:
NDVI＝(ρ NIR -ρ Red )/(ρ NIR +ρ Red )；
VC＝(NDVI - NDVI min )/(NDVI max -NDVI min )；
where NDVI represents the normalized differential vegetation index, ρ NIR And ρ Red Respectively representing the reflectivity of near red and red light wave bands of the remote sensing image;
VC denotes vegetation coverage, NDVI min And NDVI min Representing the maximum and minimum values of NDVI, respectively.
The method for evaluating the land degradation condition by fusing the multi-source remote sensing indexes comprises the following steps of:
and extracting the water and soil loss degree by using a general water and soil loss equation according to the vegetation coverage, the digital elevation model, the land utilization and coverage type, the soil type and the rainfall spatial distribution map, wherein the specific steps are as follows:
WLSE＝R·K·L·S·C·P；
wherein WLSE represents the water and soil loss degree, R is a rainfall erosion force factor, K is a soil erodibility factor, L is a slope length factor, S is a gradient factor, C is a vegetation coverage factor, and P is a water and soil conservation control measure factor.
The method for evaluating the land degradation condition by fusing the multi-source remote sensing indexes comprises the following steps of:
constructing a soil water content formula by using the humidity component of spike cap transformation:
SMC TM ＝0.0315ρ Blue +0.2021ρ Green +0.3102ρ Red +0.1594ρ NIR -0.6806ρ SWIR1 -0.6109ρ SWIR2
SMC OLI ＝0.1511ρ Blue +0.1972ρ Green +0.3283ρ Red +0.3407ρ NIR -0.7117ρ SWIR1 -0.4559ρ SWIR2 ；
wherein the SMC TM And SMC OLI Soil moisture content, rho, representing Landsat-TM and Landsat-OLI images, respectively Blue 、ρ Green 、ρ Red 、ρ NIR 、ρ SWIR1 And ρ SWIR2 Respectively representing the reflectivity of the blue, green, red, near infrared, first short-wave red light wave band and second short-wave red light wave band of the remote sensing image.
The method for evaluating the land degradation condition by fusing the multi-source remote sensing indexes comprises the following steps of:
WE＝(ρ SWIR1 -ρ BLUE )/(200-ρ SWIR1 -ρ SWIR2 )；
where ρ is BLUE 、ρ SWIR1 And ρ SWIR2 Respectively representing the reflectivity of a blue wave band, a first short wave red wave band and a second short wave red wave band of the remote sensing image.
The method for evaluating the land degradation condition by fusing the multi-source remote sensing indexes comprises the following specific steps of carrying out data standardization on image data of land utilization, land coverage, vegetation coverage, water and soil loss degree, soil water content and land wind erosion degree:
the land utilization and land coverage, the water and soil loss degree and the land wind erosion degree are positive indexes, the larger the values of the land utilization and land coverage, the water and soil loss degree and the land wind erosion degree are, the stronger the land degradation degree is, and the positive indexes are standardized as follows:
X i ＝(x i -x min )/(x max -x min )；
the vegetation coverage and the soil water content are negative indicators, the larger the vegetation coverage and the soil water content are, the weaker the land degradation degree is, and the negative indicators are standardized as follows:
X i ＝(x max -x i )/(x max -x min )；
wherein, X i Is a normalized value, x i 、x min 、x max The original value, the minimum value and the maximum value respectively represent five indexes of the land utilization and land covering, the vegetation coverage, the water and soil loss degree, the soil moisture content and the land wind erosion degree;
and the values of the land utilization and land covering, the vegetation coverage, the water and soil loss degree, the soil water content and the land wind erosion degree after data standardization are between 0 and 1.
The method for evaluating the land degradation condition by fusing the multi-source remote sensing indexes comprises the following steps of superposing image maps of land utilization, land coverage, vegetation coverage, water and soil loss degree, soil water content and land wind erosion degree, constructing a comprehensive land degradation index and carrying out function calculation:
and superposing the extracted image maps of the land utilization, the land coverage, the vegetation coverage, the water loss and soil erosion degree, the soil water content and the land wind erosion degree, and constructing a comprehensive land degradation index LDI function as follows:
LDI＝f(LULC，VC，WLSE，WE，SMC)；
the LDI is a comprehensive land degradation index, and f is an integration function of five indexes of land utilization, land coverage, vegetation coverage, water and soil loss degree, soil water content and land wind erosion degree;
the LDI function is solved by adopting a Principal Component Analysis (PCA), the Principal Component Analysis compresses an original multi-dimensional remote sensing data set, a first Component obtained by the Principal Component Analysis, namely a first Principal Component PC1, contains most of information of the original data set, and the LDI is constructed by utilizing the linear combination of the PCs 1 as follows:
LDI＝(PC1-PC1 min )/(PC1 max -PC1 min )；
wherein, PC1 and PC1 min And PC1 max Respectively representing the first principal component, the minimum value and the maximum value of the first principal component; the value of LDI is between 0 and 1, and the larger the value of LDI, the stronger the land degradation degree in the research area is.
A system for evaluating land degradation conditions by fusing multi-source remote sensing indexes comprises the following components:
the image acquisition processing module is used for acquiring remote sensing images and preprocessing the images according to the actual conditions of a research area;
the land classification processing module is used for carrying out land utilization and land coverage classification on the preprocessed remote sensing images and recoding different land types from small to large according to the influence on land degradation;
the index extraction module is used for extracting vegetation coverage, water and soil loss degree, soil water content and soil wind erosion degree from the preprocessed remote sensing image;
the standardization processing module is used for carrying out data standardization on the image data of the land utilization and land coverage, the vegetation coverage, the water and soil loss degree, the soil water content and the land wind erosion degree;
the function construction module is used for superposing the images of the land utilization, the land coverage, the vegetation coverage, the water and soil loss degree, the soil water content and the land wind erosion degree, constructing a comprehensive land degradation index and carrying out function calculation;
and the grading module is used for outputting a calculation result of the land degradation degree according to the comprehensive land degradation index and grading the land degradation condition according to the comprehensive land degradation index.
A device for evaluating land degradation conditions by fusing multi-source remote sensing indexes comprises the system for evaluating land degradation conditions by fusing multi-source remote sensing indexes as described above, and further comprises: the method comprises a memory, a processor and a program for evaluating the land degradation condition by fusing the multi-source remote sensing indexes, wherein the program is stored in the memory and can run on the processor, and when the program for evaluating the land degradation condition by fusing the multi-source remote sensing indexes is executed by the processor, the steps of the method for evaluating the land degradation condition by fusing the multi-source remote sensing indexes are realized.
A storage medium, wherein the storage medium stores a program for evaluating land degradation condition by fusing multisource remote sensing indicators, and the program for evaluating land degradation condition by fusing multisource remote sensing indicators realizes the steps of the method for evaluating land degradation condition by fusing multisource remote sensing indicators as described above when executed by a processor.
The invention discloses a method and a system for evaluating land degradation condition by fusing multi-source remote sensing indexes, wherein the method comprises the following steps: acquiring a remote sensing image and preprocessing the image according to the actual situation of a research area; carrying out land utilization and land coverage classification from the preprocessed remote sensing image, and recoding different land types according to the influence on land degradation from small to large; extracting vegetation coverage, water and soil loss degree, soil moisture content and soil wind erosion degree from the preprocessed remote sensing image; carrying out data standardization on the image data of the land utilization and land coverage, the vegetation coverage, the water and soil loss degree, the soil water content and the land wind erosion degree; superposing the image maps of the land utilization and the land coverage, the vegetation coverage, the water and soil loss degree, the soil water content and the land wind erosion degree, constructing a comprehensive land degradation index and carrying out function calculation; and outputting a calculation result of the land degradation degree according to the comprehensive land degradation index, and grading the land degradation condition according to the size of the comprehensive land degradation index. The method estimates the land degradation degree index based on remote sensing, obtains a comprehensive land degradation index by integrating a plurality of indexes, and realizes the rapid identification of the land degradation condition from the remote sensing image.
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FIG. 1 is a flow chart of a preferred embodiment of the method for evaluating land degradation by fusing multi-source remote sensing indicators according to the invention;
FIG. 2 is a schematic diagram of an implementation process of the method for evaluating land degradation by fusing multi-source remote sensing indexes;
FIG. 3 is a schematic diagram of a preferred embodiment of the system for evaluating land degradation status with integration of multi-source remote sensing indicators according to the present invention;
FIG. 4 is a schematic view of an operating environment of the apparatus for evaluating land degradation condition by fusing multi-source remote sensing indicators according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer and clearer, the present invention is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely illustrative of the invention and do not limit the invention.
The method for evaluating the land degradation condition by fusing the multi-source remote sensing indexes, disclosed by the preferred embodiment of the invention, is a method for evaluating the land degradation condition by fusing the multi-source remote sensing indexes, as shown in fig. 1 and 2, wherein the method for evaluating the land degradation condition by fusing the multi-source remote sensing indexes comprises the following steps:
and step S10, obtaining the remote sensing image and preprocessing the image according to the actual situation of the research area.
Specifically, Landsat (land satellite planning of NASA in USA) series remote sensing images can be obtained through a United States Geological Survey (USGS) website (GloVis), and image preprocessing is carried out according to the actual situation of a research area.
Further, the preprocessing comprises geometric correction, atmospheric correction, image splicing, cutting and the like, and the image preprocessing is mainly used for eliminating the problems of image radiation distortion and geometric distortion caused by satellite attitude, speed change, interaction of atmosphere and electromagnetic waves, random noise and the like in the image imaging process and for performing series of processing for meeting the requirements of research scales.
And step S20, carrying out land utilization and land coverage classification from the preprocessed remote sensing images, and recoding different land types from small to large according to the influence on land degradation.
Specifically, the preprocessed images are classified into land use/land cover (LULC) categories, for example, the images are classified into 10 categories according to "general survey content and index of geographical national conditions", including cultivated land, forest land, garden land, grassland, housing construction area, road, structure, artificial dug land, desert, bare ground and water body.
Further, different land use types are recoded according to the influence on land degradation from small to large, and the coding value is 0-1. The larger the code value, the greater the likelihood of land degradation occurring for that type of land use. The encoding is as follows: water (0), forest land (0.1), garden land (0.2), grassland (0.3), building area (0.4), cultivated land (0.5), road (0.6), structure (0.7), artificial digging land (0.8), desert and bare land (0.9 or 1).
And S30, extracting vegetation coverage, water and soil loss degree, soil moisture content and soil wind erosion degree from the preprocessed remote sensing image.
Specifically, the method for extracting Vegetation Coverage (VC) is as follows:
NDVI＝(ρ NIR -ρ Red )/(ρ NIR +ρ Red )；
VC＝(NDVI - NDVI min )/(NDVI max -NDVI min )；
where NDVI represents the normalized differential vegetation index, ρ NIR And ρ Red Are respectively provided withThe reflectivity of the near red and red light wave bands representing the remote sensing image;
VC denotes vegetation coverage, NDVI min And NDVI min Representing the maximum and minimum values of NDVI, respectively.
Wherein, the larger the VC value is, the lower the probability of land degeneration.
Specifically, the method for extracting the Water and Soil Loss degree (WLSE) comprises the following steps:
and extracting the water and soil loss degree by using a general water and soil loss equation according to the vegetation coverage, the digital elevation model, the land utilization and coverage type, the soil type and the rainfall spatial distribution map, wherein the specific steps are as follows:
WLSE＝R·K·L·S·C·P；
wherein WLSE represents the water and soil loss degree, R is a rainfall erosion force factor, K is a soil erodibility factor, L is a slope length factor, S is a gradient factor, C is a vegetation coverage factor, and P is a water and soil conservation control measure factor.
Wherein a greater WLSE value indicates a greater probability of land degradation occurring.
Specifically, the method for extracting the Soil Moisture Content (SMC) comprises the following steps:
constructing a soil water content formula by using a humidity component of spike Cap transformation (Tasseled Cap, TC):
SMC TM ＝0.0315ρ Blue +0.2021ρ Green +0.3102ρ Red +0.1594ρ NIR -0.6806ρ SWIR1 -0.6109ρ SWIR2
SMC OLI ＝0.1511ρ Blue +0.1972ρ Green +0.3283ρ Red +0.3407ρ NIR -0.7117ρ SWIR1 -0.4559ρ SWIR2 ；
wherein the SMC TM And SMC OLI Soil moisture content, rho, representing Landsat-TM and Landsat-OLI images, respectively Blue 、ρ Green 、ρ Red 、ρ NIR 、ρ SWIR1 And ρ SWIR2 Respectively represent blue, green, red, near infrared, a first short wave red light wave band and a second short wave red light wave band of the remote sensing imageReflectivity of short-wave red light wave band.
Landsat-TM represents the Thematic Mapper (TM) of the United states land resource satellite (Landsat); Landsat-OLI refers to the terrestrial Imager (OLI) of the United states terrestrial resource satellite (Landsat).
Wherein a larger SMC value indicates a lower probability of land degradation.
Specifically, the method for extracting the Wind Erosion degree (WE) of the land comprises the following steps:
WE＝(ρ SWIR1 -ρ BLUE )/(200-ρ SWIR1 -ρ SWIR2 )；
where ρ is BLUE 、ρ SWIR1 And ρ SWIR2 Respectively representing the reflectivity of a blue wave band, a first short wave red wave band and a second short wave red wave band of the remote sensing image.
Wherein a higher WE value indicates a higher probability of land degradation occurring.
And step S40, carrying out data standardization on the image data of the land utilization and land coverage, the vegetation coverage, the water and soil loss degree, the soil water content and the land wind erosion degree.
Specifically, the image data of the Land Utilization and Land Cover (LULC), Vegetation Cover (VC), water and soil loss degree (WLSE), Soil Moisture Content (SMC) and land wind erosion degree (WE) are subjected to data standardization, wherein the larger the LULC, WLSE and WE values are, the stronger the land degradation degree is, and the larger the VC and SMC values are, the weaker the land degradation degree is. The values of all indexes are made to have a uniform direction for convenient calculation, i.e. a larger value indicates a stronger degree of land deterioration.
The method specifically comprises the following steps:
the Land Utilization and Land Cover (LULC), the water and soil loss degree (WLSE) and the land wind erosion degree (WE) are positive indicators, the larger the values of the Land Utilization and Land Cover (LULC), the water and soil loss degree (WLSE) and the land wind erosion degree (WE) indicate the stronger the land degradation degree, and the positive indicators are standardized as follows:
X i ＝(x i -x min )/(x max -x min )；
the Vegetation Coverage (VC) and the Soil Moisture Content (SMC) are negative indicators, the larger the values of the Vegetation Coverage (VC) and the Soil Moisture Content (SMC) are, the weaker the land degradation degree is, and the negative indicators are standardized as follows:
X i ＝(x max -x i )/(x max -x min )；
wherein, X i Is a normalized value, x i 、x min 、x max The original value, the minimum value and the maximum value respectively represent five indexes of the Land Utilization and Land Cover (LULC), the Vegetation Coverage (VC), the water and soil loss degree (WLSE), the Soil Moisture Content (SMC) and the land wind erosion degree (WE);
and the values of the Land Utilization and Land Coverage (LULC), the Vegetation Coverage (VC), the water and soil loss degree (WLSE), the Soil Moisture Content (SMC) and the land wind erosion degree (WE) after data standardization are between 0 and 1.
And S50, superposing the images of the Land Utilization and Land Cover (LULC), the Vegetation Cover (VC), the water and soil loss degree (WLSE), the Soil Moisture Content (SMC) and the land wind erosion degree (WE), constructing a comprehensive land degradation index and performing function calculation.
Specifically, the extracted images of the Land Utilization and Land Cover (LULC), the Vegetation Cover (VC), the water loss and soil erosion degree (WLSE), the Soil Moisture Content (SMC) and the land wind erosion degree (WE) are superposed to construct a comprehensive land degradation index LDI function as follows:
LDI＝f(LULC，VC，WLSE，WE，SMC)；
the LDI is a comprehensive land degradation index, and f is an integration function of five indexes of Land Utilization and Land Coverage (LULC), Vegetation Coverage (VC), water and soil loss degree (WLSE), Soil Moisture Content (SMC) and land wind erosion degree (WE).
The redundancy of the LDI data set is eliminated by adopting a Principal Component Analysis (PCA), the Principal Component Analysis can play a role in reducing data dimensionality, the complex data set is decomposed into a few of mutually independent components (namely Principal components), each Principal Component can reflect partial information of an original variable and the contained information is not repeated, the method can be used for reducing the complex factors into a plurality of Principal components while introducing multi-aspect variables, so that the problem is simplified, more scientific and effective data information is obtained at the same time) to solve an LDI function, the original multi-dimensional remote sensing data set is compressed by Principal Component Analysis, a first Component obtained by the Principal Component Analysis, namely a first Principal Component PC1 contains most of the information of the original data set, and the LDI is constructed by utilizing the linear combination of PC1 as follows:
LDI＝(PC1-PC1 min )/(PC1 max -PC1 min )；
wherein, PC1 and PC1 min And PC1 max Respectively representing the first principal component, the minimum value and the maximum value of the first principal component; the value of LDI is between 0 and 1, and the larger the value of LDI, the stronger the land degradation degree in the research area is.
And step S60, outputting a calculation result of the land degradation degree according to the comprehensive land degradation index, and grading the land degradation condition according to the size of the comprehensive land degradation index.
Specifically, the land degradation condition can be graded according to the size of LDI, the land degradation condition can be graded into five grades of no degradation (0-0.2), light degradation (0.2-0.4), medium degradation (0.4-0.6), heavy degradation (0.6-0.8) and extreme degradation (0.8-1) according to the LDI value of an equidistant grading method, and other grades can also be graded.
The method is based on a novel Land Degradation Index (LDI) for comprehensively evaluating land degradation conditions, and provides effective support and decision for ecological environment investigation and land degradation disaster reduction; based on the idea of index comprehensive integration, a plurality of indexes which are beneficial to detecting the land degradation condition can be integrated; the method can be used for multi-scale land degeneration extraction, and solves the difficulty of manual land degeneration survey; the method is suitable for extracting the land degradation degree of the medium scale, the large scale and the small scale.
Furthermore, the constructed comprehensive LDI is not limited to be applied to Landsat series images, and other remote sensing images with similar Landsat image band information can also be applied, and are not limited to the method introduced by the invention; in addition, the five indexes of LULC, VC, WLSE, WE and SMC can be replaced by indexes with similar functions.
Further, as shown in fig. 3, based on the method for evaluating land degradation condition by fusing multi-source remote sensing indexes, the invention also correspondingly provides a system for evaluating land degradation condition by fusing multi-source remote sensing indexes, wherein the system for evaluating land degradation condition by fusing multi-source remote sensing indexes comprises:
the image acquisition processing module 101 is used for acquiring remote sensing images and preprocessing the images according to the actual conditions of a research area;
the land classification processing module 102 is used for carrying out land utilization and land coverage classification on the preprocessed remote sensing images, and recoding different land types from small to large according to the influence on land degradation;
the index extraction module 103 is used for extracting vegetation coverage, water and soil loss degree, soil water content and soil wind erosion degree from the preprocessed remote sensing image;
the standardization processing module 104 is used for carrying out data standardization on the image data of the land utilization and land coverage, the vegetation coverage, the water and soil loss degree, the soil water content and the land wind erosion degree;
the function construction module 105 is used for superposing the images of the land utilization, the land coverage, the vegetation coverage, the water and soil loss degree, the soil water content and the land wind erosion degree, constructing a comprehensive land degradation index and carrying out function calculation;
and the grading module 106 is used for outputting a calculation result of the land degradation degree according to the comprehensive land degradation index and grading the land degradation condition according to the size of the comprehensive land degradation index.
Further, as shown in fig. 4, based on the method and system for evaluating land degradation condition by fusing multi-source remote sensing indexes, the invention also correspondingly provides a device for evaluating land degradation condition by fusing multi-source remote sensing indexes, wherein the device for evaluating land degradation condition by fusing multi-source remote sensing indexes comprises the system for evaluating land degradation condition by fusing multi-source remote sensing indexes, further comprises a processor 10, a memory 20 and a display 30. FIG. 4 shows only some of the components of the apparatus for evaluating land degradation conditions incorporating multi-source remote sensing indicators, but it will be understood that not all of the shown components need be implemented, and that more or fewer components may be implemented instead.
The memory 20 may be an internal storage unit of the device for evaluating the land degradation condition by fusing the multi-source remote sensing index in some embodiments, for example, a hard disk or a memory of the device for evaluating the land degradation condition by fusing the multi-source remote sensing index. In other embodiments, the memory 20 may also be an external storage device of the apparatus for evaluating land degradation by fusing multi-source remote sensing indicators, for example, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), or the like provided on the apparatus for evaluating land degradation by fusing multi-source remote sensing indicators. Further, the memory 20 may also include both an internal storage unit and an external storage device of the apparatus for evaluating land degradation condition by the fused multi-source remote sensing index. The memory 20 is used for storing application software installed in the device for evaluating the land degradation condition by fusing the multi-source remote sensing indexes and various types of data, such as program codes of the device for evaluating the land degradation condition by fusing the multi-source remote sensing indexes. The memory 20 may also be used to temporarily store data that has been output or is to be output. In an embodiment, the memory 20 stores a program 40 for evaluating the land degradation condition by fusing the multi-source remote sensing index, and the program 40 for evaluating the land degradation condition by fusing the multi-source remote sensing index can be executed by the processor 10, so as to realize the method for evaluating the land degradation condition by fusing the multi-source remote sensing index in the application.
The processor 10 may be, in some embodiments, a Central Processing Unit (CPU), a microprocessor or other data Processing chip, and is configured to run program codes stored in the memory 20 or process data, such as executing the method for evaluating land degradation by fusing multi-source remote sensing indicators.
The display 30 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch panel, or the like in some embodiments. The display 30 is used for displaying information of the device for evaluating the land degradation condition by fusing the multi-source remote sensing indexes and displaying a visual user interface. The components 10-30 of the device for evaluating the land degradation condition by fusing the multi-source remote sensing indexes are communicated with each other through a system bus.
In one embodiment, when processor 10 executes program 40 for evaluating land degradation condition by fusing multi-source remote sensing indicators in memory 20, the following steps are implemented:
acquiring a remote sensing image and preprocessing the image according to the actual situation of a research area;
carrying out land utilization and land coverage classification from the preprocessed remote sensing image, and recoding different land types according to the influence on land degradation from small to large;
extracting vegetation coverage, water and soil loss degree, soil moisture content and soil wind erosion degree from the preprocessed remote sensing image;
carrying out data standardization on the image data of the land utilization and land coverage, the vegetation coverage, the water and soil loss degree, the soil water content and the land wind erosion degree;
superposing the image maps of the land utilization and the land coverage, the vegetation coverage, the water and soil loss degree, the soil water content and the land wind erosion degree, constructing a comprehensive land degradation index and carrying out function calculation;
and outputting a calculation result of the land degradation degree according to the comprehensive land degradation index, and grading the land degradation condition according to the size of the comprehensive land degradation index.
The invention also provides a storage medium, wherein the storage medium stores a program for evaluating the land degradation condition by fusing the multi-source remote sensing index, and the program for evaluating the land degradation condition by fusing the multi-source remote sensing index realizes the steps of the method for evaluating the land degradation condition by fusing the multi-source remote sensing index when being executed by a processor; as described above.
In summary, the invention provides a method and a system for evaluating land degradation status by fusing multi-source remote sensing indexes, wherein the method comprises the following steps: acquiring a remote sensing image and preprocessing the image according to the actual situation of a research area; carrying out land utilization and land coverage classification from the preprocessed remote sensing image, and recoding different land types according to the influence on land degradation from small to large; extracting vegetation coverage, water and soil loss degree, soil moisture content and soil wind erosion degree from the preprocessed remote sensing image; carrying out data standardization on the image data of the land utilization and land coverage, the vegetation coverage, the water and soil loss degree, the soil water content and the land wind erosion degree; superposing the image maps of the land utilization and land coverage, the vegetation coverage, the water and soil loss degree, the soil water content and the land wind erosion degree, constructing a comprehensive land degradation index and carrying out function calculation; and outputting a calculation result of the land degradation degree according to the comprehensive land degradation index, and grading the land degradation condition according to the size of the comprehensive land degradation index. The method estimates the land degradation degree index based on remote sensing, obtains a comprehensive land degradation index by integrating a plurality of indexes, and realizes the rapid identification of the land degradation condition from the remote sensing image.
Of course, it will be understood by those skilled in the art that all or part of the processes of the methods of the above embodiments may be implemented by a computer program instructing relevant hardware (such as a processor, a controller, etc.), and the program may be stored in a computer readable storage medium, and when executed, the program may include the processes of the above method embodiments. The storage medium may be a memory, a magnetic disk, an optical disk, etc.
It is to be understood that the invention is not limited to the examples described above, but that modifications and variations may be effected thereto by those of ordinary skill in the art in light of the foregoing description, and that all such modifications and variations are intended to be within the scope of the invention as defined by the appended claims.

Claims (4)

1. A method for evaluating land degradation conditions by fusing multi-source remote sensing indexes is characterized by comprising the following steps:
acquiring a remote sensing image and carrying out image preprocessing according to the actual situation of a research area;
carrying out land utilization and land coverage classification from the preprocessed remote sensing image, and recoding different land types according to the influence on land degradation from small to large;
extracting vegetation coverage, water and soil loss degree, soil moisture content and soil wind erosion degree from the preprocessed remote sensing image;
carrying out data standardization on the image data of the land utilization and land coverage, the vegetation coverage, the water and soil loss degree, the soil water content and the land wind erosion degree;
superposing the image maps of the land utilization and the land coverage, the vegetation coverage, the water and soil loss degree, the soil water content and the land wind erosion degree, constructing a comprehensive land degradation index and carrying out function calculation;
outputting a calculation result of the land degradation degree according to the comprehensive land degradation index, and grading the land degradation condition according to the size of the comprehensive land degradation index;
the method for extracting the vegetation coverage comprises the following steps:
Figure 515838DEST_PATH_IMAGE001
；
Figure 989545DEST_PATH_IMAGE002
；
wherein the content of the first and second substances,NDVIrepresents the normalized differential vegetation index and, in particular,
Figure 224217DEST_PATH_IMAGE003
and
Figure 175993DEST_PATH_IMAGE004
respectively representing remote-sensing imagesThe reflectance of the near red and red bands;
VCthe coverage of the vegetation is shown,
Figure 914142DEST_PATH_IMAGE005
and
Figure 293170DEST_PATH_IMAGE006
respectively representNDVIMaximum and minimum values of (a);
the extraction method of the water and soil loss degree comprises the following steps:
and extracting the water and soil loss degree by using a general water and soil loss equation according to the vegetation coverage, the digital elevation model, the land utilization and coverage type, the soil type and the rainfall spatial distribution map, wherein the specific steps are as follows:
WLSE=R·K·L·S·C·P ；
wherein the content of the first and second substances,WLSEthe degree of water and soil loss is shown,Ris a factor of the erosive power of rainfall, Kis a soil erodability factor and is used as a soil erodability factor,Lis a factor of the length of the slope,Sin order to be the gradient factor,Cis a vegetation coverage factor, and is characterized in that, Pa factor for water and soil conservation control measures;
the extraction method of the soil moisture content comprises the following steps:
constructing a soil water content formula by using the humidity component of spike cap transformation:
Figure 483980DEST_PATH_IMAGE007
；
wherein, the first and the second end of the pipe are connected with each other,
Figure 239447DEST_PATH_IMAGE008
and
Figure 97681DEST_PATH_IMAGE009
respectively represent the soil moisture content of the Landsat-TM and Landsat-OLI images,
Figure 913190DEST_PATH_IMAGE010
、
Figure 591296DEST_PATH_IMAGE011
、
Figure 884875DEST_PATH_IMAGE012
、
Figure 597616DEST_PATH_IMAGE013
、
Figure 584026DEST_PATH_IMAGE014
and
Figure 15008DEST_PATH_IMAGE015
respectively representing the reflectivity of blue, green, red, near infrared, a first short-wave red light wave band and a second short-wave red light wave band of the remote sensing image;
the method for extracting the wind erosion degree of the land comprises the following steps:
Figure 115206DEST_PATH_IMAGE016
；
wherein the content of the first and second substances,
Figure 416875DEST_PATH_IMAGE017
、
Figure 574186DEST_PATH_IMAGE018
and
Figure 226885DEST_PATH_IMAGE019
respectively representing the reflectivity of a blue light wave band, a first short wave red light wave band and a second short wave red light wave band of the remote sensing image;
the data standardization of the image data of the land utilization, the land coverage, the vegetation coverage, the water loss and soil erosion degree, the soil water content and the land wind erosion degree specifically comprises the following steps:
the land utilization and land coverage, the water and soil loss degree and the land wind erosion degree are positive indexes, the larger the values of the land utilization and land coverage, the water and soil loss degree and the land wind erosion degree are, the stronger the land degradation degree is, and the positive indexes are standardized as follows:
Figure 862265DEST_PATH_IMAGE020
；
the vegetation coverage and the soil water content are negative indicators, the larger the vegetation coverage and the soil water content are, the weaker the land degradation degree is, and the negative indicators are standardized as follows:
Figure 549599DEST_PATH_IMAGE021
；
wherein, is a normalized value,
Figure 612233DEST_PATH_IMAGE022
、
Figure 752227DEST_PATH_IMAGE023
、
Figure 456878DEST_PATH_IMAGE024
the original value, the minimum value and the maximum value respectively represent five indexes of the land utilization and land covering, the vegetation coverage, the water and soil loss degree, the soil moisture content and the land wind erosion degree;
the values of the land utilization and land covering, the vegetation coverage, the water and soil loss degree, the soil water content and the land wind erosion degree after data standardization are between 0 and 1;
the step of superposing the image maps of the land utilization, the land coverage, the vegetation coverage, the water loss degree, the soil water content and the land wind erosion degree, and the step of constructing a comprehensive land degradation index and carrying out function calculation specifically comprises the following steps:
the extracted land utilization and land coverage, vegetation coverage, water and soil loss degree, soil moisture content and soilSuperposing the images of the wind erosion degree of the land to construct a comprehensive land degradation indexLDIThe function is as follows:
Figure 733138DEST_PATH_IMAGE025
；
wherein the content of the first and second substances,LDIin order to synthesize the land degradation index,fthe method comprises the following steps of integrating five indexes of land utilization and land coverage, vegetation coverage, water and soil loss degree, soil water content and land wind erosion degree;
by using principal component analysis methodLDISolving the function, compressing the original multi-dimensional remote sensing data set by principal component analysis, wherein the first component obtained by the principal component analysis is the first principal componentPC1 Contains most of the information of the original data set, and utilizesPC1Linear combinatorial construction ofLDIThe following:
Figure 232253DEST_PATH_IMAGE026
；
wherein the content of the first and second substances,
Figure 859543DEST_PATH_IMAGE027
、
Figure 571147DEST_PATH_IMAGE028
and
Figure 233073DEST_PATH_IMAGE029
respectively representing the first principal component, the minimum value and the maximum value of the first principal component;LDIis between 0 and 1 and is,LDIthe larger the value, the more land degradation in the area of investigation.
2. A system for evaluating land degradation conditions by fusing multi-source remote sensing indexes is characterized by comprising the following steps:
the image acquisition processing module is used for acquiring remote sensing images and preprocessing the images according to the actual situation of a research area;
the land classification processing module is used for carrying out land utilization and land coverage classification on the preprocessed remote sensing images and recoding different land types from small to large according to the influence on land degradation;
the index extraction module is used for extracting vegetation coverage, water and soil loss degree, soil water content and soil wind erosion degree from the preprocessed remote sensing image;
the standardization processing module is used for carrying out data standardization on the image data of the land utilization and land coverage, the vegetation coverage, the water and soil loss degree, the soil water content and the land wind erosion degree;
the function construction module is used for superposing the image maps of the land utilization, the land coverage, the vegetation coverage, the water and soil loss degree, the soil water content and the land wind erosion degree, constructing a comprehensive land degradation index and carrying out function calculation;
the grading module is used for outputting a calculation result of the land degradation degree according to the comprehensive land degradation index and grading the land degradation condition according to the size of the comprehensive land degradation index;
the method for extracting the vegetation coverage comprises the following steps:
Figure 903088DEST_PATH_IMAGE030
；
Figure 752096DEST_PATH_IMAGE031
；
wherein the content of the first and second substances,NDVIrepresents the normalized differential vegetation index and, in particular,
Figure 532970DEST_PATH_IMAGE032
and
Figure 49402DEST_PATH_IMAGE033
respectively representing the proximity of the remote-sensing imageReflectance in the red and red bands;
VCthe coverage of the vegetation is shown,
Figure 624740DEST_PATH_IMAGE034
and
Figure 241271DEST_PATH_IMAGE035
respectively representNDVIMaximum and minimum values of (a);
the extraction method of the water and soil loss degree comprises the following steps:
and extracting the water and soil loss degree by using a general water and soil loss equation according to the vegetation coverage, the digital elevation model, the land utilization and coverage type, the soil type and the rainfall spatial distribution map, wherein the specific steps are as follows:
WLSE=R·K·L·S·C·P ；
wherein the content of the first and second substances,WLSEthe degree of water and soil loss is shown,Ris a factor of the erosive power of rainfall, Kis a soil erodability factor, and can be used for treating various soil erosion diseases,Lis a factor of the length of the slope,Sin order to be the gradient factor,Cis a vegetation coverage factor, and is characterized in that, Pcontrol measure factors for soil and water conservation;
the extraction method of the soil moisture content comprises the following steps:
constructing a soil water content formula by using the humidity component of spike cap transformation:
Figure 825836DEST_PATH_IMAGE036
；
wherein the content of the first and second substances,
Figure 196774DEST_PATH_IMAGE037
and
Figure 208593DEST_PATH_IMAGE038
respectively represent the soil moisture content of the Landsat-TM and Landsat-OLI images,
Figure 32192DEST_PATH_IMAGE039
、
Figure 154869DEST_PATH_IMAGE040
、
Figure 380314DEST_PATH_IMAGE041
、
Figure 563034DEST_PATH_IMAGE042
、
Figure 873929DEST_PATH_IMAGE043
and
Figure 800297DEST_PATH_IMAGE044
respectively representing the reflectivity of blue, green, red, near infrared, a first short-wave red light wave band and a second short-wave red light wave band of the remote sensing image;
the method for extracting the wind erosion degree of the land comprises the following steps:
Figure 880248DEST_PATH_IMAGE045
；
wherein, the first and the second end of the pipe are connected with each other,
Figure 233869DEST_PATH_IMAGE046
、
Figure 32061DEST_PATH_IMAGE047
and
Figure 496541DEST_PATH_IMAGE048
respectively representing the reflectivity of a blue light wave band, a first short wave red light wave band and a second short wave red light wave band of the remote sensing image;
the image data of land utilization and land cover, vegetation coverage, water and soil loss degree, soil moisture content and land wind erosion degree are carried out data standardization and specifically include:
the land utilization and land coverage, the water and soil loss degree and the land wind erosion degree are positive indexes, the larger the values of the land utilization and land coverage, the water and soil loss degree and the land wind erosion degree are, the stronger the land degradation degree is, and the positive indexes are standardized as follows:
Figure 430999DEST_PATH_IMAGE049
；
the vegetation coverage and the soil water content are negative indicators, the larger the vegetation coverage and the soil water content are, the weaker the land degradation degree is, and the negative indicators are standardized as follows:
Figure 221100DEST_PATH_IMAGE050
；
wherein the content of the first and second substances,
Figure 241009DEST_PATH_IMAGE051
is a value that is normalized to a value that,
Figure 509179DEST_PATH_IMAGE052
、
Figure 563723DEST_PATH_IMAGE053
、
Figure 262076DEST_PATH_IMAGE054
the original value, the minimum value and the maximum value respectively represent five indexes of the land utilization and land covering, the vegetation coverage, the water and soil loss degree, the soil moisture content and the land wind erosion degree;
the values of the land utilization and land covering, the vegetation coverage, the water and soil loss degree, the soil water content and the land wind erosion degree after data standardization are between 0 and 1;
the step of superposing the image maps of the land utilization, the land coverage, the vegetation coverage, the water loss degree, the soil water content and the land wind erosion degree, and the step of constructing a comprehensive land degradation index and carrying out function calculation specifically comprises the following steps:
superposing the extracted image maps of the land utilization, the land coverage, the vegetation coverage, the water loss and soil erosion degree, the soil water content and the land wind erosion degree to construct a comprehensive land degradation indexLDIThe function is as follows:
Figure 769280DEST_PATH_IMAGE055
；
wherein the content of the first and second substances,LDIin order to synthesize the land degradation index,fthe method comprises the following steps of integrating five indexes of land utilization and land coverage, vegetation coverage, water and soil loss degree, soil water content and land wind erosion degree;
by using principal component analysis methodLDIAnd solving the function, and compressing the original multi-dimensional remote sensing data set by principal component analysis, wherein the first component obtained by the principal component analysis is the first principal componentPC1 Containing most of the information of the original data set, usingPC1Linear combinatorial construction ofLDIThe following were used:
Figure 841142DEST_PATH_IMAGE056
；
wherein, the first and the second end of the pipe are connected with each other,
Figure 750192DEST_PATH_IMAGE057
、
Figure 616517DEST_PATH_IMAGE058
and
Figure 611018DEST_PATH_IMAGE059
respectively representing the first principal component, and the minimum value and the maximum value of the first principal component;LDIis between 0 and 1 and is,LDIthe larger the value is, the stronger the land degradation degree in the research area is, the stronger the vegetation coverage is extracted by the following method:
Figure 486570DEST_PATH_IMAGE060
；
Figure 515706DEST_PATH_IMAGE061
；
wherein the content of the first and second substances,NDVIrepresents the normalized differential vegetation index of the plant,
Figure 287352DEST_PATH_IMAGE062
and
Figure 34729DEST_PATH_IMAGE063
respectively representing the reflectivity of near red and red light wave bands of the remote sensing image;
VCthe coverage of the vegetation is shown in the specification,
Figure 182813DEST_PATH_IMAGE064
and
Figure 66456DEST_PATH_IMAGE065
each representsNDVIMaximum and minimum values of;
the extraction method of the water and soil loss degree comprises the following steps:
the vegetation coverage degree, the digital elevation model, the land utilization and coverage type, the soil type and the rainfall spatial distribution map are combined, the general water and soil loss equation is used for extracting the water and soil loss degree, and the method specifically comprises the following steps:
WLSE=R·K·L·S·C·P ；
wherein the content of the first and second substances,WLSEthe degree of water and soil loss is shown,Ris a factor of the erosive power of rainfall, Kis a soil erodability factor and is used as a soil erodability factor,Lis a factor of the length of the slope,Sin order to be the gradient factor,Cis a vegetation coverage factor, and is characterized in that, Pa factor for water and soil conservation control measures;
the extraction method of the soil moisture content comprises the following steps:
constructing a soil water content formula by using the humidity component of spike cap transformation:
Figure 274583DEST_PATH_IMAGE066
；
wherein the content of the first and second substances,
Figure 978097DEST_PATH_IMAGE067
and
Figure 195451DEST_PATH_IMAGE068
respectively represent the soil moisture content of the Landsat-TM and Landsat-OLI images,
Figure 933600DEST_PATH_IMAGE069
、
Figure 312629DEST_PATH_IMAGE070
、
Figure 769018DEST_PATH_IMAGE071
、
Figure 524485DEST_PATH_IMAGE072
、
Figure 385649DEST_PATH_IMAGE073
and
Figure 201158DEST_PATH_IMAGE074
respectively representing the reflectivity of blue, green, red, near infrared, a first short-wave red light wave band and a second short-wave red light wave band of the remote sensing image;
the method for extracting the wind erosion degree of the land comprises the following steps:
Figure 613685DEST_PATH_IMAGE075
；
wherein, the first and the second end of the pipe are connected with each other,
Figure 172842DEST_PATH_IMAGE076
、
Figure 885583DEST_PATH_IMAGE077
and
Figure 606415DEST_PATH_IMAGE078
respectively representing the reflectivity of a blue light wave band, a first short wave red light wave band and a second short wave red light wave band of the remote sensing image;
the data standardization of the image data of the land utilization, the land coverage, the vegetation coverage, the water loss and soil erosion degree, the soil water content and the land wind erosion degree specifically comprises the following steps:
the land utilization and land coverage, the water and soil loss degree and the land wind erosion degree are positive indexes, the larger the values of the land utilization and land coverage, the water and soil loss degree and the land wind erosion degree are, the stronger the land degradation degree is, and the positive indexes are standardized as follows:
Figure 37396DEST_PATH_IMAGE079
；
the vegetation coverage and the soil water content are negative indicators, the larger the vegetation coverage and the soil water content are, the weaker the land degradation degree is, and the negative indicators are standardized as follows:
Figure 134665DEST_PATH_IMAGE080
；
wherein the content of the first and second substances,
Figure 701913DEST_PATH_IMAGE081
is a value that is normalized to a value that,
Figure 593645DEST_PATH_IMAGE082
、
Figure 246343DEST_PATH_IMAGE083
、
Figure 881724DEST_PATH_IMAGE084
the original value, the minimum value and the maximum value respectively represent five indexes of the land utilization and land covering, the vegetation coverage, the water and soil loss degree, the soil moisture content and the land wind erosion degree;
the values of the land utilization and land covering, the vegetation coverage, the water and soil loss degree, the soil water content and the land wind erosion degree after data standardization are between 0 and 1;
the step of superposing the image maps of the land utilization, the land coverage, the vegetation coverage, the water loss degree, the soil water content and the land wind erosion degree, and the step of constructing a comprehensive land degradation index and carrying out function calculation specifically comprises the following steps:
superposing the extracted image maps of the land utilization, the land coverage, the vegetation coverage, the water loss and soil erosion degree, the soil water content and the land wind erosion degree to construct a comprehensive land degradation indexLDIThe function is as follows:
Figure 569057DEST_PATH_IMAGE085
；
wherein the content of the first and second substances,LDIin order to synthesize the land degradation index,fthe method comprises the following steps of integrating five indexes of land utilization and land coverage, vegetation coverage, water and soil loss degree, soil water content and land wind erosion degree;
by using principal component analysis methodLDISolving the function, compressing the original multi-dimensional remote sensing data set by principal component analysis, wherein the first component obtained by the principal component analysis is the first principal componentPC1 Containing most of the information of the original data set, usingPC1Linear combinatorial construction ofLDIThe following were used:
Figure 897271DEST_PATH_IMAGE086
；
wherein, the first and the second end of the pipe are connected with each other,
Figure 771686DEST_PATH_IMAGE087
、
Figure 210757DEST_PATH_IMAGE088
and
Figure 752597DEST_PATH_IMAGE089
respectively representing the first principal component, the minimum value and the maximum value of the first principal component;LDIis between 0 and 1 and is,LDIthe larger the value, the greater the degree of land degradation in the area of investigation.
3. An apparatus for evaluating land degradation condition by fusing multi-source remote sensing index, wherein the apparatus for evaluating land degradation condition by fusing multi-source remote sensing index comprises the system for evaluating land degradation condition by fusing multi-source remote sensing index according to claim 2, further comprising: the method comprises the steps of a memory, a processor and a program for evaluating the land degradation condition by fusing the multi-source remote sensing indexes, wherein the program for evaluating the land degradation condition by fusing the multi-source remote sensing indexes is stored in the memory and can run on the processor, and when the program for evaluating the land degradation condition by fusing the multi-source remote sensing indexes is executed by the processor, the method for evaluating the land degradation condition by fusing the multi-source remote sensing indexes is realized according to claim 1.
4. A storage medium, wherein the storage medium stores a program for evaluating land degradation by fusing multisource remote sensing indicators, and the program for evaluating land degradation by fusing multisource remote sensing indicators realizes the steps of the method for evaluating land degradation by fusing multisource remote sensing indicators as claimed in claim 1 when executed by a processor.
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