CN108170926B - Information data acquisition and analysis method for river grassland degradation condition - Google Patents
Information data acquisition and analysis method for river grassland degradation condition Download PDFInfo
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Abstract
The invention discloses an information data acquisition and analysis method for the degradation condition of valley grassland, which comprises the following specific steps: the method comprises the steps of obtaining NDVI data, obtaining DEM data, processing information, converting data, inverting vegetation coverage, dividing grassland degradation grades, analyzing grassland degradation cold/hot spots, analyzing grassland degradation space-time variation, distributing and evolving grassland degradation cold/hot spots, differentiating the altitude of the grassland degradation of river valleys, analyzing grassland coverage and analyzing grassland degradation factors, wherein the areas with obvious grassland degradation grades in the measured areas can be integrally analyzed through a grassland degradation grade division diagram, and the data analysis is more visual; cold/hot spot analysis shows that the spatial difference between grassland degradation and improvement is more obvious; through grassland coverage analysis, the degradation characteristics of grasslands expressing different coverage grades can be quantitatively analyzed; through the analysis of the climate temperature, the rainfall and the grazing situation, the analysis of the grassland degradation situation is more accurate.
Description
Technical Field
The invention relates to the technical field of grassland degradation improvement, in particular to an information data acquisition and analysis method for the degradation condition of valley grassland.
Background
Under unreasonable utilization, the process of reverse succession and productivity reduction of the grassland ecosystem is called grassland deterioration and is also called grassland deterioration. The main manifestations are the height, coverage, yield and quality reduction of grassland vegetation, the deterioration of soil habitat, and the decline of production capacity and ecological function. The deterioration of the grass over a long period of time and in a wide range causes not only a decrease in the productivity of the grass itself but also a deterioration in the ecological environment and a threat to the survival and development of humans. Under the influence of adverse natural factors such as drought, sand wind, water erosion, salt and alkali, waterlogging, underground water level change and the like, or unreasonable utilization such as excessive grazing and mowing, or excessive digging, excessive cutting and woodman harvesting damage grassland vegetation, the grassland ecological environment is deteriorated, the biological yield of the grassland pasture is reduced, the quality is reduced, the utilization performance of the grassland is reduced, and even the process of losing the utilization value is called grassland deterioration. Grass deterioration includes grass deterioration, desertification and salinization.
The grassland degradation has severe influence on the environment, and the existing grassland degradation information acquisition and processing method is too simple and has low applicability. However, in China, the valley regions are many, and the common grassland degradation information acquisition and processing method cannot reasonably analyze the complex terrain of the valley regions, has inaccurate data, low accuracy and low credibility of data processing results, and cannot be suitable for managing the valley grassland.
Disclosure of Invention
The invention aims to provide an information data acquisition and analysis method for the degradation condition of the river valley grassland, which is used for carrying out information acquisition and processing from multiple aspects and grading data analysis and processing to obtain accurate and high-reliability data and solve the problems, and the information data acquisition and analysis method for the degradation condition of the river valley grassland comprises the following specific steps:
s1: acquiring a vegetation coverage index of a measured area, and acquiring NDVI data of the measured area by a remote sensing technology;
s2: acquiring digital elevation model data of the terrain of a measured area, and acquiring DEM data of the measured area through a GPS, a total station, an aerospace image or an existing topographic map;
s3: the information processing, namely performing data format conversion, mosaic, projection conversion and research area extraction processing on the obtained NDVI and DEM data;
s4: converting data, namely converting the livestock stock quantity data into standard sheep units, and converting the quantities of goats, donkeys, cattle and horses according to the proportion of 0.8, 3, 5 and 6 respectively;
s5: and (3) vegetation coverage inversion, wherein the grassland vegetation coverage is inverted by using a pixel binary model, and the calculation formula is as follows:
s6: dividing grassland degradation grades, and dividing the grassland degradation degrees into 5 grades of non-degradation, light degradation, moderate degradation, severe degradation and extreme degradation according to the standard and the percentage of the grassland vegetation coverage degree relative to the non-degradation grassland vegetation coverage degree;
s7: Cold/Hot Point analysis of lawn degradation using Getis-Ord GiAnalyzing the spatial pattern of the 'hot spots' and the 'cold spots' of the deteriorated grassland, and analyzing the evolution characteristics of the deteriorated grassland, wherein the calculation formula is as follows:
s8: analyzing the grassland deterioration space-time change, making grassland deterioration grade distribution maps of regions measured in three periods according to the dividing standard of the grassland deterioration grade in S6 and the calculation method thereof, and counting the areas and the proportions of grasslands with different deterioration grades in each period;
s9: the grassland degraded cold/hot spot distribution and evolution, the grade to be improved in the grassland degraded grade is further subdivided into light improvement, moderate improvement, high improvement and extremely high improvement, 4 grades and 5 grades in S6 form codes of 9 grades, and the values of pixel elements Gi one by one are calculated through the codes of 9 grades, so that a degraded grassland cold/hot spot distribution graph is obtained;
s10: making a scatter diagram of the grassland vegetation coverage change proportion of three periods changing along with the elevation through a degraded grassland cold/hot spot distribution diagram in S9 and a grassland degradation grade distribution diagram of an area measured in three periods in S8;
s11: grassland coverage analysis, namely, according to a grassland vegetation coverage change proportion scatter diagram in S10, making a relationship diagram of grassland degradation and grassland coverage, dividing the grassland coverage into 5 grades of low coverage, medium coverage and high coverage according to the sum of 20%, 40%, 60% and 80%, counting the proportion of grassland with each coverage grade in each degradation grade, and analyzing the relationship between the grassland degradation and the grassland coverage and the influence of saturation defects of NDVI on the grassland degradation;
s12: grassland degradation factor analysis, namely acquiring rainfall and air temperature data in three periods according to the three periods mentioned in S8, making a line graph by taking time as a horizontal axis, performing conversion statistics according to the livestock stock quantity in S4, making a data line graph by taking time as the horizontal axis, and analyzing by referring to the line graph;
s13: and summarizing, inverting the vegetation coverage according to MODIS NDVI data and the pixel binary model, analyzing the space-time change characteristic of the grassland degradation, and drawing a conclusion.
Preferably, the NDVI data mentioned in S1 is long-time sequential MODIS data.
Preferably, in S1, in order to reduce interference of noise information such as cloud cover, Savitzky-Golay filtering processing is performed on the NDVI sequence data of 23 th year, and the annual NDVI data is synthesized by a maximum value synthesis method (MVC method), and the annual vegetation growth status is represented by the annual vegetation NDVI maximum value.
Preferably, in S3, to maintain data accuracy and make the NDVI data consistent with the DEM data pel size, the annual NDVI data and DEM are resampled to 50m x 50 m.
Preferably, the calculation formula in S5 is that FcIndicating vegetation coverage of the grassland, NDVIsoilFor the pure bare soil of research areaNDVI value of the pixel, NDVIvegThe NDVI value of the pixel is covered by pure vegetation, and according to the characteristics of the NDVI image histogram in the research area, the NDVI value of 5 percent of the NDVI image histogram in the research area is taken as the NDVI in the conversion process of the NDVI and the coveragesoilTaking the NDVI value at 95% as the NDVIvegThe value is obtained.
Preferably, the formula of S7, wherein xjIs a grassy deterioration level code of pixel j, wi，jThe spatial weight defined by a distance rule between the pixels i and j is defined, the adjacent spatial range is 1, the non-adjacent spatial range is 0, n is the total number of pixels, and in addition:
the statistics are that the higher the z score is, the more the high-value pixel elements of the hot spot are gathered, and the lower the z score is, the more the low-value pixel elements of the cold spot are gathered.
Compared with the prior art, the invention has the beneficial effects that: the invention utilizes MODIS NDVI data and a pixel binary model to invert vegetation coverage, and reasonably analyzes the space-time change characteristics of grassland degradation:
1. through the grassland degradation grade division diagram, the regions with obvious grassland degradation grade in the measured region can be analyzed on the whole, and the data analysis is more visual;
2. the cold/hot spot analysis shows that the cold/hot pattern of the river valley grassland deterioration is changed from the contrast of 'deterioration' and 'unchanged' to the contrast of 'deterioration' and 'improvement', and the spatial difference of the grassland deterioration and the improvement is more obvious;
3. in the aspect of altitude differentiation, the grassland information conditions of different altitudes can be intuitively analyzed;
4. through grassland coverage analysis, the degradation characteristics of grasslands expressing different coverage grades can be quantitatively analyzed;
5. through the analysis of the climate temperature, the rainfall and the grazing situation, the analysis of the grassland degradation situation is more accurate.
Drawings
FIG. 1 is a schematic illustration of a study area according to an embodiment of the present invention;
FIG. 2 is a graph of the grass degradation level in Ili valley 2001-2015 in accordance with an embodiment of the present invention;
FIG. 3 is a diagram showing the distribution of cold/hot spots of deteriorated grassland in Ili river valleys according to an embodiment of the present invention;
FIG. 4 is a scattergram of the ratio of the change in the vegetation coverage of grasslands according to the embodiment of the present invention;
FIG. 5 is a scatter plot showing the ratio of the change in vegetation coverage of grasslands according to the embodiment of the present invention;
FIG. 6 is a graph showing the temperature and precipitation changes in the valley of Ili river in 2001-2015;
FIG. 7 is a graph showing the variation of livestock stocking capacity in Yili river valley in 2001-2015 in accordance with an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Example 1
The following is a specific implementation process of the invention in the Yili valley of Xinjiang in China.
Yili valley is located at northwest of Xinjiang and at the border of China, and due to special topographic features, the Yili valley can intercept humid water vapor brought by land west wind, so that the Yili valley becomes the largest oasis in Xinjiang and an important water source place of Yili river in cross-country rivers. Abundant rainfall creates favorable conditions for vegetation growth, and the grassland development in Ili river valley is the type of ground surface covering with the widest area, so that important guarantee is provided for the protection of regional biodiversity, the conservation of water sources and the maintenance of ecological balance. Meanwhile, the grassland resources in the valley are high in quality, so that the grassland resources become famous pasturing areas in China. However, as the economy of the Ili river valley develops and the population increases, the interference of human activities on the grassland ecology is also increasing, and the problems of grassland deterioration such as reduction of grassland area and reduction of productivity caused thereby are gradually worsening, which seriously affect the ecological stability of the area and the healthy development of animal husbandry. Therefore, the research on the quantitative characteristics of the grass land degradation of the Ili river valley and the analysis of the time-space change and the influence factors thereof have important guiding significance on the establishment and implementation of the grass ecological protection measures in the area. In recent years, researchers have made relevant studies on the dynamic changes of Ili valley vegetation, but have lacked extensive studies on grass deterioration.
The remote sensing technology is a main means for quantitative research on large-scale surface vegetation coverage. The Normalized Differential Vegetation Index (NDVI) is linearly related to the coverage of surface Vegetation, and can quantitatively reflect the dynamic characteristics of Vegetation, so that the method is widely used for monitoring Vegetation dynamics and grassland degradation. Currently, the long-time sequence NDVI data commonly used are NOAA-AVHRR NDVI and TERRA-MODIS NDVI. Compared with the AVHRR data, the MODIS data have a shorter time series, but have a higher spatial resolution, and can reflect the spatial differences of the surface vegetation in more detail, so that the MODIS data are more advantageous in quantifying the watershed vegetation coverage with a smaller spatial scale.
Aiming at the practical problem of grassland degradation in the Ili valley area, the method utilizes MODIS remote sensing image data to invert the grassland vegetation coverage of the Ili valley 2001-2015 year, refers to grassland degradation evaluation standards formulated by the country based on the vegetation coverage, analyzes and evaluates the grass land degradation degree of the Ili valley, and combines DEM ((Digital Elevation Model) terrain data to count the space-time change rule of the grassland degradation to provide scientific basis for dynamic monitoring, ecological protection and sustainable development and utilization of the Ili valley grassland.
1 overview of the study region
The Ili river valley is positioned between 80 degrees 09 '42' to 84 degrees 56 '50' E, 42 degrees 14 '16' to 44 degrees 53 '30' N, is positioned in the center of the continental Europe, and is surrounded by three mountains of east, south and north in the whole area, the terrain is complex, the river is comprehensive, and 5 regional units of Ili valley land, sclrenergic valley land, Texas valley land, Hilska valley land and Shosu basin land are distributed. The Yili valley terrain is high in east and low in west, narrow in east and wide in west, and is trumpet-shaped and open to west, and the humid water vapor in the west wind zone is forced to rise and condense under the influence of the terrain to form vigorous rainfall, so that the west region wet island becomes a west region wet island. The whole area is controlled by continental climate and mountain climate in the middle temperature zone, the annual average precipitation is 800mm, the annual average temperature is 2.9-9.1 ℃, and the annual average sunshine hours are 2700-3000 h. Vegetation distribution is influenced by terrain, and is developed by five types of vegetation, namely desert, grassland, meadow, forest and hidden vegetation, the meadow vegetation develops well and is a high-quality meadow in Xinjiang, and main grassland plants include artemisia selengensis (seriphigenium transfer), wood skin (kochia prostrata (L.), polygonum bulbiferum (Viviparaum L.), Dactylis glomerata (Dactylis glomerata), Potentilla falcate (Potentilla chrysantha), alfalfa (Medicafalcate), Stipa capillata (Stipa capitata), Carex (Carex lipocarpopos), Poa pratense (Poa pratensensis), and Iris japonica (Iris ruinica).
2 materials and methods
2.1 data Source and Pre-processing
The NDVI data was MODIS MOD13Q1 product, released by NASA EOS data center, USA, with spatial resolution of 250m, temporal resolution of 16d, 23 years per year, time series of 1 month 2001 to 12 months 2015, totaling data at stage 345. The DEM data is SRTM data at 90m spatial resolution measured jointly by the National Aeronautics and Space Administration (NASA) and the National Institute of Mapping (NIMA). Weather data of three national reference weather stations of Yining, Shosu and Nile are from a weather data center of the China weather bureau, and comprise monthly synthetic data of air temperature and precipitation. The livestock stock data are from the annual book of statistics in the autonomous region of Kazak, Ili and Xinjiang Uygur.
And carrying out data format conversion, mosaic, projection conversion, research area extraction and the like on the obtained NDVI and DEM data. In order to reduce the interference of noise information such as cloud coverage, Savitzky-Golay filtering processing is carried out on NDVI sequence data in 23 th year, annual NDVI data are synthesized by a maximum value synthesis method (MVC method), and the annual vegetation growth condition is represented by the maximum value of the annual vegetation NDVI. And finally, in order to keep the data precision and make the NDVI data consistent with the pixel size of the DEM data, resampling the annual NDVI data and the DEM to be 50m multiplied by 50 m.
To convert the stock quantity data to standard sheep units, the numbers of goats, donkeys, cattle and horses were translated in the proportions of 0.8, 3, 5 and 6, respectively.
2.2 methods of investigation
2.2.1 vegetation coverage inversion
The pixel binary model is used for inversion research of grassland vegetation coverage, and the calculation formula is as follows:
in the formula: fcIndicating vegetation coverage of the grassland, NDVIsoilNDVI value, NDVI, of pure bare soil pixels in the research areavegAnd covering the NDVI value of the pixel element for pure vegetation. According to the characteristics of the NDVI image histogram in the research area and referring to the previous experience, in the process of converting the NDVI and the coverage, taking the NDVI value of 5 percent of the NDVI image histogram in the research area as the NDVIsoilTaking the NDVI value at 95% as the NDVIvegThe value is obtained.
2.2.2 grassland degradation rating
Grassland deterioration is a relative concept, and calculating the grassland deterioration degree by using the change of the vegetation coverage of two single years before and after the grassland deterioration degree brings large errors to the grassland deterioration evaluation due to random fluctuation of the vegetation coverage. Under the influence of dry and wet conditions, the vegetation coverage of the Ili river valley has large annual fluctuation, and in order to weaken the influence of random fluctuation of vegetation growth on grassland degradation estimation caused by good and bad hydrothermal conditions in a single year, the grassland coverage time series data in the 2001 + 2015 year are divided into three time periods, namely 2001 + 2005, 2006 + 2010 and 2011 + 2015 years, the average value in 5 years represents the vegetation coverage level in each time period, and the vegetation coverage levels in the 2001 + 2005 time period and the 2006 + 2010 time period are respectively used as judgment references to analyze the spatio-temporal change characteristics of the Ili river valley grassland degradation in three periods of 2001 + 2010 (2006 + 2010 to 2001 + 2005) and 2001 + 2015 (2011 + 2015 to 2001 + 2010) and 2006 + 2015 (2011 + 2015 to 2006 + 2010).
The grading index of natural grassland deterioration, desertification and salinization (the national standard 19377-2003) classifies the grassland deterioration degree into 5 grades of non-deterioration, light deterioration, moderate deterioration, severe deterioration and extremely severe deterioration according to the percentage of the grassland vegetation coverage relative to the non-deterioration grassland vegetation coverage. The undegraded grass fields were refined to 2 levels with reference to this standard to show the spatial characteristics of the Ili valley fields in detail, the detailed levels and division criteria are shown in Table 1.
TABLE 1 grassland deterioration rating and Classification Standard
2.2.3 lawn degradation Cold/Hot Point analysis
Cold/hot analysis is used to identify spatial clusters of high (hot spots) and low (cold spots) values with statistical significance. The Getis-Ord Gi analysis is used herein to identify the spatial pattern of "hot spots" and "cold spots" of deteriorated grass, and to analyze its evolution characteristics. The calculation formula is as follows:
wherein xjIs a grassy deterioration level code of pixel j, wi，jThe spatial weight defined by a distance rule between the pixels i and j is defined, the adjacent spatial range is 1, the non-adjacent spatial range is 0, n is the total number of pixels, and in addition:
3 results and analysis
3.1 Ili valley grassland degradation spatio-temporal variation analysis
According to the dividing standard of the grassland degradation grade and the calculation method thereof, a spatial distribution diagram (figure 2) of the grass degradation grade of the Ili river valley in three periods in the year 2001-2015 is prepared, and the areas and the proportions of the grasslands with different degradation grades in each period are counted (table 2).
As can be seen from fig. 2, the deteriorated grassland of the Ili river valley occupied a large range in the whole period of 2001-2015, the Hongshan plain, the low mountain or the medium mountain area on both sides of the 4 rivers of the Ili river, the sclenssi river, the Kahshi river and the Texas river in the valley, and the periphery of the Shosu basin is the main distribution area of the deteriorated grassland. According to the statistical results in Table 2, the area of the deteriorated grassland of the Ili river valley within 15 years is 114.25 × 104hm2, which accounts for 46.18% of the total grassland area, wherein the area is 104.10 × 104hm2, which accounts for 33.33% of the total grassland area, and the deteriorated grassland bodies are distributed in low mountains or high mountainous areas in the area; the proportion of other grades is smaller, the proportion of moderate degree, severe degree and extremely severe deterioration is respectively 8.80%, 3.33% and 0.72%, and the proportions are mainly distributed in the flood plains and the low mountain areas on the two sides of the downstream valley of the river; the area of the improved grassland accounts for 1.41 percent and is distributed in the area near the exit of the North Yili river.
TABLE 2 grassland area and proportion of different degradation grades
For two different periods between the year 2001-2010 and the year 2006-2015, the proportion of unchanged grassland is increased from 72.84% to 75.71%, and the total proportion of deteriorated grassland is also decreased from 25.78% to 17.5%, but this does not indicate that the deterioration trend of grassland is twisted, but only indicates that the condition of the grassland in the year 2001-2010 is maintained by a large area of grassland, the area of the deteriorated grassland is still continuously expanded, and the net increased deterioration area reaches 54.65 × 104hm2 and occupies 17.5% of the total area of the grassland. Spatially, the grassland in the mountains in the west of amulera on the south of the downstream of the karhshi maintains the early state, while the grassland in the mountains on both sides of the valley of the downstream of the tex starts to deteriorate (fig. 2). In addition, in the year of 2006-2015, the deterioration degree of the grassland on the south downstream of the sclens river and on the two sides of the valley of the Yili river is improved, the improved area reaches 21.22 multiplied by 104hm2, and the proportion reaches 6.79 percent of the total area of the grassland. The reduction of the area proportion of deteriorated grassland and the increase of the area of the grassland indicate that although the deterioration trend of the grassland is not changed, the deterioration speed of the grassland is obviously reduced during the year 2006-2015 compared with the year 2001-2010, and the benefit of the grassland restoration measures such as returning grassland, fencing and grazing forbidding begins to be gradually shown.
In addition, according to topographic features of the Ili river valley, compared with the spatial distribution of the deteriorated grassland between the years 2001-2015 and 2010 in FIG. 2, the spatial distribution of the deteriorated grassland of the Ili river valley gradually expands from the downstream of the river to the midstream and the upstream of the river, expands from the river valley plains and the low mountain areas on both sides of the river valley to the middle and high mountain areas, the area of the deteriorated grassland gradually expands, the unchanged grassland not only gradually reduces the area, but also gradually disperses and breaks up the grassland patches of each grade. This indicates that the tendency of the grass land in the Ili river valley to deteriorate has not been stopped, the utilization mode of the predatory grass resources has not been changed significantly, and the ecological protection of the grass land and the sustainable development and utilization of the grass resources still bear great pressure.
3.2 Ili valley deteriorated grassland Cold/Hot Point distribution and evolution
The cold/hot spot analysis can more intuitively display the spatial characteristics and the dynamics of the deteriorated grassland of the Ili valley. To enhance the contrast between "high value" and "low value" and facilitate the comparative analysis of "cold spot" and "hot spot" areas, the improvement in grass deterioration of 6 levels was further subdivided into 4 levels of mild, moderate, high and very high improvement, prior to the cold/hot spot analysis. The detailed grades, grade codes and division criteria are shown in table 3.
The Ili valley deteriorated grassland is graded according to the grading standards in tables 1 and 3, and the pixel-by-pixel values are calculated by using codes of 9 grades
Value of cold/hot spots on deteriorated grasslandLayout (fig. 3).
TABLE 3 improvement of grass grades and division criteria
As can be seen from FIG. 3, the "cold spot" and "hot spot" accumulation features are evident during the years 2001-2010 and 2001-2015, and are mainly composed of very cold regions (Z < -2.58) and very hot regions (Z > 2.58), wherein the very cold regions respectively account for 24.41% and 31.75% of the total area of the grassland, and the very hot regions respectively account for 68.04% and 52.97%; spatially, the "cold spots" overlap the distribution of deteriorated grass, the direction of expansion of which also coincides with the expansion of deteriorated grass; the "hot spots" do not converge on the grass-improving zone and overlap with the zones of unchanged grass, the proportion of grass-improving zone in the "hot spots" during the two periods being only 1.02% and 1.35%, while the proportion of unchanged grass is 91.98% and 73.53%. For the period of 2006 + 2015, the proportion of the 'hot spots' is obviously reduced, but is consistent with the distribution area of the improved grassland, and the proportion of the improved grassland in the 'hot spot' aggregation accounts for 33.63%; the "cold spot" distribution was consistent with the deteriorated grass distribution, but contained 67.12% of the unchanged grass.
Through the analysis, the dynamic evolution that the Ili river valley deteriorated grassland has the 'cold spot' enhancement and the 'hot spot' weakening is shown in 2001-2015 years, and the evolution process is consistent with the space-time change of the deteriorated grassland; however, the distribution of "hot spots" in the cold/hot spot analysis results does not represent the aggregation characteristics of "high value" (grassland improvement area), but reflects the aggregation characteristics of the unchanged grassland, because the proportion of the area of the grass improved by the valley of Ili during 2001 + 2015 is too small (only 1.41%), most of the area is occupied by the unchanged grassland (52.41%) and the degraded grassland (46.18%), and a cold-hot contrast pattern of "degraded" and "unchanged" is formed; however, during the year 2006-2015, 33.63% of the "hot spot" distribution area was occupied by "high values" of improved grass, and the proportion of area of deteriorated grass represented by "low values" in the "cold spot" distribution area was also reduced, the pattern of "cold spots" and "hot spots" showing a shift in the characteristics of the cold and hot patterns compared to "deterioration" and "improvement". Therefore, in 2006-2015, the characteristics of the grass land degradation of the Ili river valley are not only shown in that the degradation speed is reduced, but also different from the change trends mainly degraded in 2001-2010 and 2001-2015, a spatial difference between the degradation and the improvement is generated, which indicates that the single change trend mainly degraded is changed, and the change trend is more diversified.
3.3 elevation differentiation of Ili valley grassland deterioration
As can be seen from the figures 2 and 3, the elevation difference of the Italy river valley grassland degradation is obvious, and in order to find out the difference rule of the elevation change of the grassland degradation, scatter diagrams (figure 4) of the grassland vegetation coverage change proportion along with the elevation change in three periods of 2001 + 2010, 2006 + 2015 and 2001 + 2015 are made.
In fig. 4, the intersection point of the boundary line of the degradation levels of different grasslands and the minimum value of the variation ratio of the vegetation coverage corresponding to each altitude is the critical altitude of the degradation level, and the variation of the critical altitude of each degradation level in different years reflects the spatial and temporal difference of the grassland degradation with the altitude. As can be seen from the figure, the extremely heavily deteriorated grass is below the 1100m elevation line for three periods, comparable to the critical elevation of the improved grass; and the critical altitude of other grades is increased, wherein the critical altitude of the severe deteriorated grassland is expanded from 1250m to 1500m, the distribution of the moderate deteriorated grassland is expanded from 1500m to about 2100m, the main distribution of the mild deterioration is in the ranges of 750-2250m and 3000-3500m in the year of 2001-2010, and then the whole range of 750-3600m is expanded.
TABLE 4 area and proportion of deteriorated grassland in different altitude zones
Along with the increase of the critical altitude of the grassland of each degradation grade, the distribution area of the degraded grassland in each altitude zone is gradually increased. According to the calculation results in table 4, in the altitude band below 1500m, the total area of deteriorated grassland is gradually increased from 54.13 × 104hm2 to 61.32 × 104hm2, and increased by 7.19 × 104hm2, and the increase ratio is 13.28%; within the altitude zone of 1500-3000m, deteriorated grassland is gradually increased from 23.45 × 104hm2 to 76.71 × 104hm2, the area is increased from 53.26 × 104hm2, the area is increased by 2.27 times, the increased area and the increased proportion are both far greater than the area below the altitude zone of 1500m, and the area becomes the area with the most obvious deteriorated and expanded grassland; in the area with the altitude higher than 3000m, although the distribution area of the deteriorated grassland is smaller, the area is greatly increased, namely the area is increased from 2.99 × 104hm2 to 6.22 × 104hm2, and is increased by 3.23 × 104hm2, and the area is increased by 1.08 times.
Discussion 4
4.1 Effect of saturation defects of NDVI on evaluation of lawn deterioration
NDVI has the defect of easy saturation in vegetation high-coverage areas, so that the NDVI reduces estimation results in applications such as crop yield estimation and grassland biomass inversion. Based on this, a relational graph of grassland deterioration and grassland coverage was prepared using the data of the proportion of grassland coverage change between 2001-2015 years and the data of the grassland coverage during the period of 2001-2005 years (fig. 5), and the grassland coverage was divided into 5 levels of low coverage, medium coverage, and high coverage according to thresholds of 20%, 40%, 60%, and 80% of low coverage, and the proportions of grassland at each coverage level within each deterioration level were counted (table 5), the relation of grassland deterioration and grassland coverage was analyzed, and the influence of saturation defect of NDVI on the evaluation of grassland deterioration was investigated.
As can be seen in FIG. 5, the deterioration of grass gradually decreased as the vegetation coverage increased. Very severe deterioration, severe deterioration and improved grass mainly low-coverage grass with a coverage of less than 20% and medium-coverage grass with a coverage of 20% -40%, wherein 88.41% of the very severe deteriorated grass are low-coverage grass, 30.94% and 54.48% of the severe deterioration and 52.13% and 34.75% of the improved grass are low-and medium-low coverage grass; while slightly deteriorated and unchanged grass fields are mainly medium-high-coverage grass fields with a coverage of 60-80% and high-coverage grass fields with a coverage of more than 80%, wherein 30.01% and 46.25% of the slightly deteriorated grass fields are medium-high and high-coverage grass fields, and 20.94% and 68.4% of the unchanged grass fields are medium-high-coverage grass fields and high-coverage grass fields; the major causes of moderate deterioration are low and medium and high coverage grasses, and high and medium coverage grasses, which are 35.61%, 33.16% and 17.18% respectively. The characteristic that the degree of deterioration of grass varies with vegetation indicates that grass with low coverage deteriorates more than grass with high coverage.
TABLE 5 degradation rating ratio in grasslands of different coverage ratings
It can also be seen from the degree of dispersion of the different proportions of variation of the coverage in figure 5 that the lower the coverage of the vegetation, the higher the degree of dispersion, indicating that grasses with high coverage respond less strongly to deterioration of the grasses than grasses with low coverage. This is due, on the one hand, to the fact that the proportion of grass with high coverage changes is less than that of grass with low coverage for a reduction in the equivalent grass yield, and on the other hand, to the fact that the reduction in grass yield with high coverage is not fully expressed and the degree of deterioration is underestimated due to saturation defects in NDVI.
It can be seen from the above analysis that the method for evaluating the grassland degradation by utilizing the NDVI to invert the vegetation coverage can underestimate the grassland degradation due to weak reaction of the grassland with high coverage on the reduction of the grass yield, and influences the sensitivity of the method for evaluating the grassland degradation in the high-coverage area of the grassland.
4.2 Ei-Plough valley grassland deterioration influencing factors
The hydrothermal condition is a determinant factor influencing the growth of the grassland vegetation, and the annual change of the hydrothermal condition can influence the growth condition of the grassland vegetation. Through analysis of air temperature and precipitation of 3 national reference meteorological stations in the Ili river valley (figure 6), the average precipitation in three time intervals of 2001 + 2005, 2006 + 2010 and 2011 + 2015 is 463.02mm, 422.22mm and 392.37mm respectively, the average air temperature is 7.15 ℃, 7.60 ℃ and 7.05 ℃, the precipitation shows a continuous reduction trend, the air temperature is reduced, the hydrothermal condition evolves towards the cold dry direction, and the deterioration of the hydrothermal condition promotes the degradation of the Ili river valley grassland to a certain extent. In addition, although the precipitation in the high altitude area of the Ili river valley is relatively abundant, the temperature is relatively low, so the temperature stress is stronger than that in the precipitation, and the opposite is true in the desert area with low altitude. According to the change of the average temperature and the precipitation in the three periods of time in the graph 6, compared with the change of the average temperature and the precipitation in 2001-2005, in 2006-2010, the precipitation is reduced, although the growth condition of the vegetation in the high-altitude area is improved to a certain extent by the change of the hydrothermal condition, the stress on the vegetation in the desert area is aggravated, the growth of the vegetation in the low-altitude area in the period of time is inhibited, and the degradation of the grassland with the low coverage area is promoted to a certain extent; 2011-2015 the temperature and precipitation are reduced, the vegetation growth conditions in high-altitude areas and low-altitude areas are deteriorated, and the grassland degradation in the whole area is further aggravated.
The Ili river valley is an important stock production base in China, but traditional grazing still accounts for an absolute proportion of the production mode, so that the increase of the number of livestock means the increase of the actual stock carrying capacity of the grassland. Statistically, the livestock stock stocking amount of the Ili river valley in 2001-2015 is increased from 1146.89X 104 sheep units to 1369.77X 104 sheep units, and is increased by 19.43% in 15 years (figure 7). According to related researches, the livestock of 1 sheep unit in the Ili river valley needs 0.52hm2 grassland for feeding, namely, the livestock carrying capacity per hectare of grassland is 1.92 sheep units, and according to the calculation results of the livestock stocking numbers and the total grassland area of 2001 and 2015 years of the Ili river valley, the livestock amount actually fed per hectare of grassland in 2001 is 3.54 sheep units (the total grassland area is 324.40 multiplied by 104hm2 calculated according to the area of 2000 years counted by Zhanghong flag), and the total grassland area is increased to 4.39 by 2015, which is far beyond the livestock carrying capacity of the grassland. Excessive grazing has a direct relationship with grassland deterioration, and it is seen that extensive livestock production, rapid growth of livestock stocks and the resulting long-term excessive grazing are the main causes of the continued aggravation of the grassland deterioration in the Ill valley.
In addition, in 2010, the state department decides to implement a grassland ecological protection and assistance reward mechanism with a period of five years in 8 provinces (regions) such as Xinjiang from 2011, and rewards and assists in grass-animal balance, grassland grazing prohibition and herdsman production. Based on this policy, the state of illinois performed grazing in 2011 and 2013 on desert-like grasslands and important water sources of 15.88 × 104hm2, and performed livestock transfer placement on 292.73 × 104hm2 grasslands, expecting to achieve herd balance. The implementation of measures such as grazing prohibition, livestock transfer and placement and the like creates favorable conditions for the restoration of grassland ecology, so that the grassland deterioration area in the period of 2006-2015 is obviously reduced compared with the grassland deterioration area in the period of 2001-2010, and the grassland area subjected to improvement is also greatly increased.
Based on the above analysis, the areas of the deteriorated grass in the Ill valley during 2006-2015 were significantly reduced and the areas of the improved grass were greatly increased, whereas the herding pressure in the Ill valley was not reduced during this period, but rather increased (FIG. 7), so it is concluded that the situation of increased herding pressure and decreased deterioration of grass occurred due to a change in the spatial distribution of the grass grazing intensity. The above inference is drawn because: 2011-2015 period meadow improvement distribution areas are mainly distributed in low-altitude valley plains and mountain front flood base sectors (2006-2015 period chart in fig. 2), the areas are mainly low-coverage meadows such as desert and desert grassland, the meadows with low coverage are sensitive to the change of grazing pressure, the meadows are improved only due to the reduction of grazing pressure, therefore the improvement of the grassland coverage area is inevitably due to the fact that the grazing pressure is transferred to other places, namely to areas with relative high altitude, the areas with relatively high altitude are high-coverage meadows such as mountain meadows, meadow grassland and grassland, the NDVI of high-coverage meadow vegetation is easily saturated, the reduction of the meadow coverage is easily low to estimate the reduction of the meadow yield, and therefore, under the situation of increasing the grazing pressure, the reduction of the meadow yield is limited even if the meadow yield is obviously reduced, even though it appears as an unchanged case, this is consistent with the relative changes in the spatial distribution of grass deterioration in the period of 2006-2015 and the period of 2001-2015 in fig. 2; the area in fig. 2 where the grassland coverage does not change in the 2011-2015 period relative to the 2006-2010 period shows degeneration in comparison with the 2001-2005 period, which shows that in the case of increased grazing pressure, the decrease in the grassland coverage relative to the 2006-2010 period shows no change as the degradation criterion is not exceeded, but the decrease in the 2001-2005 period shows continuous degeneration as the degradation criterion is exceeded.
The situation that the grazing pressure is increased and the reduction amount of the grassland coverage is limited in the Ili river valley grassland is also verified again that the method for evaluating the grassland degradation by utilizing the NDVI to invert the vegetation coverage has the defect of weak sensitivity to the grassland degradation of the high-vegetation-coverage area.
5 conclusion
The method utilizes MODIS NDVI data and a pixel binary model to invert vegetation coverage, and researches the space-time change characteristics of lawn degradation in the year of Yili valley 2001 + 2015. The following conclusions were made:
1) the Yili valley grassland has a continuous degradation trend during 2001-2015 years, 46.18% of the grassland is degraded to different degrees during 15 years, but the light degradation is mainly performed on the whole; the spatially mild deteriorated grassland is mainly distributed in the middle mountains and the middle and high mountainous areas on the two sides of the river, and other deteriorated grades are mainly distributed in the flood plains and the low mountainous areas on the two sides of the valley downstream of the river. The deterioration speed of the grassland is obviously slowed down in the period of 2010-2015, and the large-area grassland of the flood plains positioned at the south downstream of the sclerse river and at the two sides of the whole valley of the Ili river is even improved.
2) Cold/hot spot analysis showed that the cold/hot pattern of grass degradation in Ili valley shifted from "degraded" to "unchanged" to "degraded" to "improved" contrast, the spatial difference between grass degradation and improvement became more pronounced, with a single trend of change dominated by degradation changed.
3) In the aspect of altitude differentiation, the grassland degradation degree is gradually reduced along with the increase of the altitude, and the distribution range is gradually expanded to a high-altitude area. The grassland degeneration is mainly severe, severe and moderate in the area below the elevation 1500m, and is mainly mild in the area above 1500 m; the deteriorated grassland is increased by 13.28 percent in the elevation zone below 1500m, and the deteriorated grassland is increased by 2.27 times in the elevation zone of 1500-3000m, which is the most obvious area of the deteriorated grassland in the valley of Ili river, and the area of the deteriorated grassland is smaller but increased by 1.08 times in the area above the elevation zone of 3000 m.
4) The degradation characteristics of the grasslands with different coverage grades have larger difference, and the degradation degree of the grasslands is gradually reduced along with the improvement of the planted and covered grasslands; the light saturation defect of NDVI in high vegetation coverage areas and the poor sensitivity of high coverage vegetation coverage to livestock stress increases limit the quantitative expression of turf degradation by methods for evaluating turf degradation by inverting vegetation coverage using NDVI.
5) The long-term over-grazing is the main reason for the continuous aggravation of the grass land in the Ili river valley, and the continuously worsened climatic conditions and fluctuation changes thereof are also important factors for promoting the grass land degeneration; the implementation of the grassland protection policy provides favorable conditions for grassland restoration, and is a main reason for the gradual degradation and diversified change trend of the grassland.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.
Claims (4)
1. A method for acquiring and analyzing information data of river valley grassland degradation conditions is characterized by comprising the following steps: the information data acquisition and analysis method for the river valley grassland degradation condition comprises the following specific steps:
s1: acquiring a vegetation coverage index of a measured area, and acquiring NDVI data of the measured area by a remote sensing technology;
s2: acquiring digital elevation model data of the terrain of a measured area, and acquiring DEM data of the measured area through a GPS, a total station, an aerospace image or an existing topographic map;
s3: the information processing, namely performing data format conversion, mosaic, projection conversion and research area extraction processing on the obtained NDVI and DEM data;
s4: converting data, namely converting the livestock stock quantity data into standard sheep units, and converting the quantities of goats, donkeys, cattle and horses according to the proportion of 0.8, 3, 5 and 6 respectively;
s5: and (3) vegetation coverage inversion, wherein the grassland vegetation coverage is inverted by using a pixel binary model, and the calculation formula is as follows:
wherein, FcIndicating vegetation coverage of the grassland, NDVIsoilNDVI value, NDVI, of pure bare soil pixels in the research areavegThe NDVI value of the pixel is covered by pure vegetation, and according to the characteristics of the NDVI image histogram in the research area, the NDVI value of 5 percent of the NDVI image histogram in the research area is taken as the NDVI in the conversion process of the NDVI and the coveragesoilTaking the NDVI value at 95% as the NDVIvegA value;
s6: dividing the grassland degradation grade into 5 grades of nondegradation, light degradation, moderate degradation, severe degradation and extreme degradation;
s7: Cold/Hot Point analysis of lawn degradation using Getis-Ord GiAnalyzing the spatial pattern of the 'hot spots' and the 'cold spots' of the deteriorated grassland, and analyzing the evolution characteristics of the deteriorated grassland, wherein the calculation formula is as follows:
the calculation formula in S7, wherein xjIs a grassy deterioration level code of pixel j, wi，jThe spatial weight defined by a distance rule between the pixels i and j is defined, the adjacent spatial range is 1, the non-adjacent spatial range is 0, n is the total number of pixels, and in addition:
the statistics is that the higher the z-value score is, the more the high-value pixels of the hot spot are gathered, and the lower the z-value score is, the more the low-value pixels of the cold spot are gathered;
s8: analyzing the grassland deterioration space-time change, making grassland deterioration grade distribution maps of regions measured in three periods according to the dividing standard of the grassland deterioration grade in S6 and the calculation method thereof, and counting the areas and the proportions of grasslands with different deterioration grades in each period;
s9: distributing and evolving degraded grassland cold/hot spots, dividing the degraded grassland grade into light improvement, moderate improvement, high improvement and extremely high improvement, 4 grades and 5 grades in S6 to form 9 grade codes, and calculating the value of pixel Gi one by one through the 9 codes to obtain a degraded grassland cold/hot spot distribution diagram;
s10: making a scatter diagram of the grassland vegetation coverage change proportion of three periods changing along with the elevation through a degraded grassland cold/hot spot distribution diagram in S9 and a grassland degradation grade distribution diagram of an area measured in three periods in S8;
s11: grassland coverage analysis, namely, according to a grassland vegetation coverage change proportion scatter diagram in S10, making a relationship diagram of grassland degradation and grassland coverage, dividing the grassland coverage into 5 grades of low coverage, medium coverage and high coverage according to the sum of 20%, 40%, 60% and 80%, counting the proportion of grassland with each coverage grade in each degradation grade, and analyzing the relationship between the grassland degradation and the grassland coverage and the influence of saturation defects of NDVI on the grassland degradation;
s12: grassland degradation factor analysis, namely acquiring rainfall and air temperature data in three periods according to the three periods mentioned in S8, making a line graph by taking time as a horizontal axis, performing conversion statistics according to the livestock stock quantity in S4, making a data line graph by taking time as the horizontal axis, and analyzing by referring to the line graph;
s13: and summarizing, inverting the vegetation coverage according to MODIS NDVI data and the pixel binary model, analyzing the space-time change characteristic of the grassland degradation, and drawing a conclusion.
2. The method of claim 1, wherein the method comprises the steps of: the NDVI data mentioned in S1 adopts long time series MODIS data.
3. The method of claim 1, wherein the method comprises the steps of: in S1, in order to reduce interference of noise information such as cloud cover, Savitzky-Golay filter processing is performed on the NDVI sequence data of 23 th-year-old period, annual NDVI data is synthesized by a maximum value synthesis method (MVC method), and the annual vegetation growth status is represented by the annual vegetation NDVI maximum value.
4. The method of claim 1, wherein the method comprises the steps of: in S3, to maintain data accuracy and make the NDVI data consistent with the DEM data pel size, the annual NDVI data and DEM are resampled to 50m x 50 m.
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| CN109211791B (en) * | 2018-08-10 | 2020-09-22 | 北京观微科技有限公司 | Crop growth monitoring method and system |
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| CN110348108A (en) * | 2019-07-08 | 2019-10-18 | 青海大学 | A method of evaluation Grassland degradation degree |
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| CN112381288B (en) * | 2020-11-13 | 2022-07-05 | 西北民族大学 | Ecological management system for grassland in alpine regions |
| CN113361398B (en) * | 2021-06-04 | 2022-10-18 | 内蒙古工业大学 | Grassland fence identification method and device and storage medium |
| CN113376123B (en) * | 2021-06-13 | 2023-08-01 | 福州大学 | Cultivated land waste information extraction method based on vegetation-pigment-soil time sequence coupling dispersion |
| CN115082793B (en) * | 2022-06-28 | 2023-02-17 | 生态环境部卫星环境应用中心 | Method and device for rapidly investigating space background condition of forest and grass in water source area |
| CN115294460B (en) * | 2022-10-08 | 2023-01-17 | 杭州领见数字农业科技有限公司 | Method for determining degradation degree of phyllostachys praecox forest, medium and electronic device |
| CN116012733B (en) * | 2022-12-14 | 2023-09-29 | 兰州大学 | Method for repairing degenerated alpine grassland bare spot by utilizing species combination of native grass |
| CN116090674B (en) * | 2023-04-07 | 2023-07-18 | 中国科学院空天信息创新研究院 | Early warning method, device and system for grassland degradation |
| CN117522956B (en) * | 2023-11-20 | 2024-07-02 | 青海大学 | Method for calculating degradation indication grass seed coverage by using unmanned aerial vehicle image |
| CN117744951B (en) * | 2024-02-08 | 2024-05-03 | 中国科学院大气物理研究所 | Grassland degradation evaluation method, device, equipment and medium based on remote sensing data |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106934233A (en) * | 2017-03-09 | 2017-07-07 | 江西理工大学 | A kind of rare-earth mining area environmental pressure quantitative estimation method and system based on PSR models |
-
2017
- 2017-12-12 CN CN201711398439.4A patent/CN108170926B/en not_active Expired - Fee Related
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106934233A (en) * | 2017-03-09 | 2017-07-07 | 江西理工大学 | A kind of rare-earth mining area environmental pressure quantitative estimation method and system based on PSR models |
Non-Patent Citations (6)
| Title |
|---|
| 2000-2014年豫西山地植被NDVI时空变化特征分析;张静静 等;《河南科学》;20150831;第33卷(第8期);全文 * |
| Evaluating suitability of MODIS-Terra images for reproducing historic sediment concentrations in water bodies: Lake Tana;EthiopiaEssayas Kaba 等;《International Journal of Applied Earth Observation and Geoinformation》;20140228;全文 * |
| 基于MODIS NDVI的三峡库区植被覆盖度动态监测;吴昌广 等;《林业科学》;20120131;第48卷(第1期);全文 * |
| 基于MODIS 数据的山西省六大煤田区植被覆盖度时空变化特征及其驱动力分析;张彦彬 等;《干旱区地理》;20160131;第39卷(第1期);全文 * |
| 基于MODIS的青藏高原地表参数时空变化研究;郭鹏;《中国优秀硕士学位论文全文数据库 基础科技辑》;20110415;全文 * |
| 基于SPOT NDVI的祁连山草地植被覆盖时空变化趋势分析;戴声佩 等;《地理科学进展》;20100930;第29卷(第9期);全文 * |
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