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Environmental Earth Sciences

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Open Access 01.01.2024 | Original Article

Dynamic monitoring of vegetation growth in Engebei ecological demonstration area based on remote sensing

verfasst von: Jie Zhang, Zhichao Yang, Shulin Zheng, Haijun Yue

Erschienen in: Environmental Earth Sciences | Ausgabe 2/2024

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Abstract

Vegetation growth and the ecological environment affect each other. Understanding vegetation growth trend and their spatial pattern evolution is of great significance for ecological environment assessment and protection. The spatial trend and pattern evolution of different vegetation growth in the ecological area are included in vegetation planning, which can be used as a reference for vegetation planning and development. Based on Landsat remote sensing data, the spatial evolution trend and dynamic evolution of vegetation patterns at different levels in the Engebei Ecological Demonstration Zone from 2000 to 2021 were revealed, and the degree of ecological vulnerability was determined. The results show that: during the growing season from 2000 to 2021, the vegetation growth of the study area showed an overall improvement trend (about 63.44%), and the vegetation growth trends was mainly not significant change (about 97.12%). The improvement of vegetation growth in the southern desert region was higher than that in the northern arid steppe region. In the future, the main growth types were non-significant improvement of sustainability, non-significant improvement of anti-sustainability, non-significant degradation of anti-sustainability, and non-significant degradation, accounting for 38.84%, 23.81%, 17.30%, and 17.16%, respectively. The spatial distribution of vegetation of different grades mainly showed aggregation patterns, and the spatial distribution evolvement was more and more uniform, the distribution range was gradually expanded, and the orientation was gradually weakened. The numerical clustering degree of ecological vulnerability of vegetation was constantly declining. The results can provide a scientific basis for vegetation planning in the Engebei ecological demonstration area.

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Introduction

The land cover vegetation in arid areas often presents patchy spatial patterns (Aguiar and Sala 1999), and the study of its structure and evolution is of great significance to reveal the process of ecosystem change in Engebei (Liu 2020). Vegetation is an important part of an ecosystem and an intermediate link of a soil–plant–atmosphere continuum. As the largest green economy, the vegetation ecological environment can meet people’s growing demand for green products and play an irreplaceable role in enriching people’s income and carbon peak (Xiwang et al. 2018). Most of the existing studies on vegetation growth and spatial pattern change have introduced the linear trend method and the spatial assessment method to describe the integrity and complexity of the vegetation system. However, there are few studies on the succession of vegetation patch formation based on the local relationship between different levels of vegetation, and there is a lack of discussion on the integrity and difference of vegetation change trends in small areas. At present, migration and diffusion are the main forms of plant adaptation to climate change (Parmesan 2006). The spatial pattern of vegetation is formed through long-term mutual feedback with the environment and is the result of the interaction between ecological and hydrological processes. It is of vital ecological significance to deeply explore the dynamic changes of vegetation patches and apply them to the improvement of vegetation with poor growth according to their spatial distribution (Du et al. 2012).

Engebei ecological demonstration zone is located at Dalat Banner, Ordos City, Inner Mongolia. It is located in the northern margin of the middle section of the Kubuqi Desert, one of the eight major deserts in China. With unique natural habitats and natural resources, it is an important ecological barrier between deserts and towns. The Engebei research area has two parts: a desert ecological area and arid grassland ecological area. The arid environment causes great pressure on plant growth and propagation, and the spatial heterogeneity is strong (Ma and Zhou 2008). Since ecological construction was carried out in the 1980s, desertification control has been carried out by generations, so appropriate indicators are adopted to evaluate the vegetation growth and ecosystem status in this area. Taking stable natural vegetation patterns as a reference, improving degraded vegetation is of great significance for maintaining the ecological security barrier of the Engebei. Based on Landsat remote sensing data, the evolution trend of vegetation growth and the prediction of its future growth in Engebei study area from 2000 to 2021 were counted, and the spatial distribution of vegetation patches of different grades was analyzed statistically to evaluate the state of the vegetation system.

Materials and methods

Study area

Ordos Engebei ecological demonstration zone is located at Dalat Banner, Ordos City, Inner Mongolia, as shown in Fig. 1, with a boundary of 109°17ʹ 20ʹʹ–109°28′ 00ʺ east longitude and 40°18ʹ 30ʺ–40°26′ 20ʺ north latitude. The landform is divided into three types: high south, low north, high west, and low east. Wind erosion and accumulation from North Liang, middle beach, and Nansha. Sunshine is sufficient, there are about more than 3100 h of illumination each year, the annual average temperature is about 7 °C, the average rainfall is about 348.3 mm, the average evapotranspiration is about 2506.3 mm, the average wind speed varies between 3.2 and 3.3 m/s. The demonstration area belongs to the transition zone of desert and arid steppe, and the aeolian sand is the main soil in the whole demonstration area. Limited by ecological environment conditions, the natural vegetation distribution is mainly sandy and hardy plants.

Data source

As shown in Table 1, the data used in this study included Landsat 5//8 remote sensing images. To ensure the reliability of data and comparability of results, remote sensing images with low cloud and vegetation growth season (June and August) in the study area were selected.

Table 1

Remote sensing data

Satellite	Date	Satellite	Date
Landsat 5 TM	August 31, 2000	Landsat 5 TM	July 23, 2003
Landsat 5 TM	July 31, 2006	Landsat 5 TM	July 23, 2009
Landsat 8 OLI	August 3, 2013	Landsat 8 OLI	July 24, 2015
Landsat 8 OLI	June 14, 2018	Landsat 8 OLI	August 9, 2021

Research method

Trend analysis

(1) Sen slope estimation and Mann–Kendall non-parametric test method Mann–Kendall is a non-parametric test method, which can be used to analyze samples that do not follow a normal distribution but are not interfered with by outliers. For specific formulas, see literature (Zhihui 2012).

Sen slope estimation is a robust non-parametric statistical trend calculation method. Insensitive to outlier data and error measurement, it is often used in trend analysis of long-time series data. The formula is as follows (Xiong and Juntao 2020):

$$\beta = {\text{Median}}\left( {\frac{{x_{j} - x_{i} }}{j - i}} \right) \forall j > i.$$

(1)

(2) Hurst index Hurst is an index calculated based on the re-scale range analysis (R/S) method, which can effectively and quantitatively describe the change persistence of long time series. Hurst index can reflect the relationship between changes in vegetation before and after, and the specific formula principle is shown in the literature (Hurst 1951; Guining 2022). When H = 0.5, it indicates that the autocorrelation coefficient of NDVI is 0, that is, the changes before and after the time series are irrelevant, and the data have random characteristics. When 1 > H > 0.5, it indicates the persistent change of NDVI time series and is positively correlated. The closer the value is to 1, the stronger the persistence is. When 0 < H < 0.5, it indicates that the change of time series is negatively correlated, and this series has anti-persistence. The closer the value is to 0, the greater the anti-persistence is. At the same time, to obtain the double information on the vegetation change trend and consistency test, ArcGIS was used for superposition analysis, and the changing trend of NDVI was combined with the Hurst index.

Spatial evolution analysis

(1) Orientation distribution standard deviation ellipse (SDE) and center of gravity migration model are spatial statistical methods to reveal various characteristics of variable spatial distribution (Wang et al. 2015). The center of gravity is calculated as follows (Lingbo 2019):

$$M\left( {\overline{X},\overline{Y}} \right) = \left[ {\frac{{\sum\nolimits_{i = 1}^{n} {w_{i} } X_{i} }}{{\sum\nolimits_{i = 1}^{n} {w_{i} } }}\frac{{\sum\nolimits_{i = 1}^{n} {w_{i} } Y_{i} }}{{\sum\nolimits_{i = 1}^{n} {w_{i} } }}} \right]$$

(2)

$${\text{SDE}}_{x} = \sqrt {\frac{{\sum\nolimits_{i = 1}^{n} {\left( {x_{i} - \mathop X\limits^{ - } } \right)}^{2} }}{n}}$$

(3)

$${\text{SDE}}_{y} = \sqrt {\frac{{\sum\nolimits_{i = 1}^{n} {\left( {y_{i} - \overline{Y}} \right)^{2} } }}{n}}$$

(4)

$$\tan \theta = \frac{{\left( {\sum\nolimits_{i = 1}^{n} {\tilde{x}_{i}^{2} } - \sum\nolimits_{i = 1}^{n} {\tilde{y}_{i}^{2} } } \right) + \sqrt {\left( {\sum\nolimits_{i = 1}^{n} {\tilde{x}_{i}^{2} } - \sum\nolimits_{i = 1}^{n} {\tilde{y}_{i} }^{2} } \right)^{2} + 4\left( {\sum\nolimits_{i = 1}^{n} {\tilde{i}_{i} \tilde{y}_{i} } } \right)^{2} } }}{{2\sum\nolimits_{i = 1}^{n} {\tilde{x}_{i} \tilde{y}_{i} } }}$$

(5)

$$\left( {\frac{x}{{{\text{SDE}}_{x} }}} \right)^{2} + \left( {\frac{y}{{{\text{SDE}}_{y} }}} \right)^{2} = S.$$

(6)

(2) Average Nearest Neighbor Index The average nearest neighbor tool measures the distance between the center of gravity of each element and the center of gravity of the nearest element and then calculates the average of all these distances. At the same time, the index of the nearest neighbor will be obtained. If the index of the nearest neighbor is less than 1, the default attribute of the performance is clustering. If the exponent is greater than 1, the properties of the representation tend to be discrete. The formula is as follows (Ebdon 1985):

$$\left. {\begin{array}{*{20}c} {{\text{ANN}} = \frac{{\overline{D}_{o} }}{{\overline{D}_{E} }}} \\ {\overline{D}_{o} = \frac{{\sum\nolimits_{i = 1}^{n} {d_{i} } }}{n}} \\ {\overline{D}_{E} = \frac{0.5}{{\sqrt {{n \mathord{\left/ {\vphantom {n A}} \right. \kern-0pt} A}} }}} \\ \end{array} } \right\}$$

(7)

$$\left. {\begin{array}{*{20}c} {z = \frac{{\mathop D\limits^{ - }_{o} - \mathop D\limits^{ - }_{E} }}{SE}} \\ {{\text{SE}} = \frac{0.26136}{{\sqrt {n^{2} /A} }}} \\ \end{array} } \right\}.$$

(8)

(3) Getis Ord Gi* statistical data are widely used for hotspot analysis, and hotspot (${\text{Gi}}^{*}$) analysis has been applied to explore vulnerable vegetation zones with NDVI concentration as the input parameter. The method provides seven geospatial model distribution zones (Gi Bin) of vegetation area within the range of percentage of the confidence level of heat and cold. 99% cold confidence level as extremely vulnerable, 95% cold confidence level as very vulnerable, 90% cold confidence level as vulnerable, on the other hand, 99% heat confidence level as extremely safe, 95% heat confidence level as very safe, and 90% heat confidence level as safe. In addition, other clustering levels (non-significance) were assumed to be stable. The calculation equation is as follows (Crist and Malila 1980):

$$Gi^{*} = \frac{{\sum\nolimits_{j = 1}^{n} {w_{ij} } x_{j - } \overline{x}\sum\nolimits_{j = 1}^{n} {w_{ij} } }}{{\sqrt[3]{{\left[ {{{n\sum\nolimits_{j = 1}^{n} {w_{ij}^{2} } \left[ {\left( {\sum\nolimits_{j = 1}^{n} {w_{ij}^{2} } } \right)} \right]} \mathord{\left/ {\vphantom {{n\sum\nolimits_{j = 1}^{n} {w_{ij}^{2} } \left[ {\left( {\sum\nolimits_{j = 1}^{n} {w_{ij}^{2} } } \right)} \right]} {(n - 1)}}} \right. \kern-0pt} {(n - 1)}}} \right.}}}}$$

(9)

$$\overline{X} = \frac{1}{N}\sum\nolimits_{j = 1}^{n} {x_{j} } \sqrt {\left( {\frac{1}{n}\sum\nolimits_{j = 1}^{n} {x_{j}^{2} } - \overline{X}^{2} } \right)} .$$

(10)

Results

Vegetation growth and sustainable distribution characteristics

Evolution trend of vegetation growth

Figure 2 and Table 2 show the significant trend of vegetation change in the Engebei research area during 2000–2021, obtained by pixel based on the Sen + Mann–Kendall method. According to the Z value significance test, the vegetation growth trend of the Engebei research area has obvious aggregation, and the area of significant improvement is very small and scattered, only 0.42 km², accounting for about 0.79% of the total area of the study area. The areas where vegetation growth was not significantly improved were highly clustered, mainly distributed in the southern desert area of the study area, and less disturbed by human beings, with an area of 33.14 km², accounting for 62.65% of the total area of the study area. The area of vegetation growth degradation was mainly scattered in the northern part of the study area, with an area of 1.07 km², accounting for 2.01% of the total area of the study area. The areas with no significant vegetation growth degradation were mainly clustered in the northern arid steppe of the study area, and a few scattered in the southern desert, covering an area of 18.23 km², accounting for 34.46% of the total area of the study area. The improvement of vegetation growth in the southern arid steppe region was higher than that in the northern desert region. The degradation of vegetation growth occurred in the northern region due to human activities, but the overall vegetation situation tended to improve.

Table 2

Sen + MK trend of vegetation growth in Engebei from 2000 to 2021

Sen + MK	Number of pixels	Area (km²)	Proportion (%)
Marked improvement (β ≥ 0, \|Z\|> 1.96)	465	0.42	0.79
Insignificant improvement (β > 0, \|Z\|≤ 1.96)	37,027	33.14	62.65
Significant degradation (β ≤ 0, \|Z\|> 1.96)	1190	1.07	2.01
Inconspicuous degradation (β < 0, \|Z\|≤ 1.96)	20,367	18.23	34.46

R/S analysis of vegetation growth

The Hurst index of Engebei’s research area is shown in Fig. 3. The Hurst mean value of the research area is about 0.52, and the range is between 0.16 and 1.02. It can be seen from Table 3 that the regional area of vegetation growth in the future will be positive and sustainable (Hurst > 0.5), accounting for about 57.68%. In the study area, 42.24% of the vegetation growth area showed an anti-sustainability trend (Hurst < 0.5). The results indicated that there was little difference between the area of sustainability and anti-sustainability of future vegetation growth in the study area, which would present different degrees of agglomeration distribution in the whole study area.

Table 3

Hurst Index of vegetation growth in Engebei from 2000 to 2021

Hurst	Number of pixels	Area (km²)	Proportion (%)
Antisustainability (0.0 < H < 0.5)	24,961	22.34	42.24
Persistence (0.5 < H < 1.0)	34,088	30.51	57.68

Future sustainability analysis of vegetation growth

The forecast types and area statistics of future vegetation growth in the Engebei research area are shown in Fig. 4 and Table 4. The results show that the future vegetation growth in the Engebei research area is mainly divided into four types: no significant improvement of discontinuity, no significant improvement of sustainability, no significant degradation of discontinuity, and no significant degradation of sustainability. Accounting for 23.81%, 38.84%, 17.30%, and 17.16% of the total area of the study area, respectively; the areas with no significant improvement in anti-sustainability and non-significant improvement in sustainability were mainly distributed in the southern desert area of the study area. However, the regions of non-significant degradation and non-significant degradation were mainly distributed in the arid steppe region in the northern part of the study area. Since the ecological construction was carried out in Engebei, more than 20 years of efforts have been made to improve the regional ecological and environmental conditions in the arid barren land with very low net birth yield by afforestation, windbreak, and sand fixation. Therefore, the vegetation growth trend has become better and will have a certain sustainability in the future.

Table 4

Vegetation future growth forecast types in Engebei from 2000 to 2021

Prediction type	Number of pixels	Area (km²)	Proportion (%)
Significant improvement in sustainability (β ≥ 0, \|Z\|> 1.96, 0.0 < H < 0.5)	111	0.10	0.19
There was no significant improvement in anti-sustainability (β > 0, \|Z\|≤ 1.96, 0.0 < H < 0.5)	14,074	12.60	23.81
Sustained significant improvement (β ≥ 0, \|Z\|> 1.96, 0.5 < H < 1.0)	354	0.32	0.60
No significant improvement in persistence (β > 0, \|Z\|≤ 1.96, 0.5 < H < 1.0)	22,953	20.55	38.84
Significant degradation of discontinuity (β ≤ 0, \|Z\|> 1.96, 0.0 < H < 0.5)	552	0.49	0.93
No significant degradation against sustainability (β < 0, \|Z\|≤ 1.96, 0.0 < H < 0.5)	10,224	9.15	17.30
Persistent significant degradation (β ≤ 0, \|Z\|> 1.96, 0.5 < H < 1.0)	638	0.57	1.08
No significant degradation in persistence (β < 0, \|Z\|≤ 1.96, 0.5 < H < 1.0)	10,143	9.08	17.16

Evolution of vegetation spatial distribution pattern and ecological vulnerability

Directional distribution change

Geometric centers and standard deviation ellipses of vegetation of different levels at two-time nodes in 2000 and 2021, as shown in Fig. 5 and Table 5, can be seen in the development direction and scope of vegetation growth of different levels:

Good vegetation growth: In 2000, the standard deviation of the well-growing vegetation area was elongated in an ellipse shape, short in short axis, and large in difference in short and short semi-axis. At this stage, the spatial distribution range of the well-growing vegetation was narrow, and the spatial distribution variability was large. The main trend of the well-growing vegetation was scattered in the east–southeast direction, and the oblateness of the ellipse was 71.54, indicating obvious directivity. In 2021, the standard deviation ellipse of the well-growing vegetation area was longer on the short axis, the ellipse area was significantly expanded, and the difference between the short and short semi-axes was relatively small. In this stage, the spatial distribution range of the well-growing vegetation expanded compared with that in 2000, while the spatial variability decreased and the directivity weakened, and the main trend was roughly east–northeast axial dispersion. Between 2000 and 2021, the center of gravity of the well-grown vegetation in the study area shifted to a large extent, about 1843 m, and the center of gravity of the main trend changed from 113.33° to 83.17°, showing a general trend of centralized expansion to the northwest.

Vegetation growth is good: The standard deviation ellipse of the vegetation region with good growth in 2000 is flat and long, which is similar to that of the vegetation region with good growth in 2000, and the difference between the length and length along the semi-axis is large. The spatial distribution range of the vegetation with good growth in this stage is narrow, and the variability of the spatial distribution is large. The main trend of the vegetation with good growth is roughly east–southeast axial dispersion. The oblateness of the ellipse is 69.27%, and the directivity is obvious. However, the vegetation area with good growth in 2021 has a relatively obvious expansion, while the ellipticity is low, that is, the spatial distribution of the vegetation with good growth in 2021 is more uniform, and the improved range of vegetation growth has a relatively obvious improvement. The main trend is broadly eastward. In 2021, compared with 2000, the center of gravity of the vegetation area with good growth moved about 669 m, and the center of gravity azimuth changed from 69.27° to 39.39°, showing a trend of centralized expansion in the southeast direction.

Vegetation growth is stable: The standard deviation ellipses of the regions with stable vegetation growth in 2000 and 2021 are similar, and the standard distances between the long axis and the short axis are also similar, but there are still significant differences in spatial distribution. The main trend is broadly northeast axial dispersion. From 2000 to 2021, the barycenter azimuth of the vegetation with stable growth changed from 77.88° to 66.64°, and the barycenter migrated about 771 m, showing a general trend of southwest migration.

Vegetation growth is poor: In 2000, the standard deviation ellipse of the vegetation area with poor growth was rounder, and there was little difference between the length and length of the semi-axis. In this stage, the vegetation with poor growth had a wide spatial distribution range, the main trend of vegetation was scattered in the southeast axial direction, the ellipse oblateness was small, and the direction was not obvious. However, in 2021, the standard deviation of vegetation with poor growth was flatter and the ellipse area was reduced. Meanwhile, the difference between long and short axes was large. Compared with 2000, the spatial distribution range of vegetation with poor growth in this stage was significantly reduced, and the main trend of vegetation was roughly east axial dispersion. Elliptic oblateness is slightly larger and directivity is more obvious. The barycenter azimuth of the vegetation with poor growth varied from 99.47° to 88.63°, and the barycenter migrated about 2002 m, showing a general trend of northeast migration.

Vegetation growth difference: The standard deviation of vegetation area with a long period of potential difference in 2000 was more circular, and the difference between the length and length of the semi-axis was small. At this stage, the vegetation with poor growth had a wide spatial distribution range, and the main and secondary vegetation trends were roughly dispersed in the east and north axial directions, respectively. Elliptic oblateness is small and directivity is not obvious. In 2021, the standard deviation ellipse shape of vegetation with poor growth was flatter than that in 2000, the ellipse area was greatly reduced, and the difference between the length and length of the semi-axis was small. In this stage, the spatial distribution of vegetation with poor growth was significantly reduced compared with that in 2000, and the main and secondary trends of vegetation were roughly dispersed in the east and north axial direction, respectively, and the ellipse oblateness was only slightly larger than that in 2000, showing similar directionally. The barycenter azimuth of vegetation with poor growth varied from 83.41° to 82.94°, and the barycenter migrated about 3248 m, showing a trend of northeast migration.

Table 5

Vegetation direction distribution of different growth patterns in Engebei from 2000 to 2021

Years	Elliptic circumference (m)	Elliptic area (m²)	X-standard distance(m)	Y-standard distance(m)	Roundness	Center of gravity distance(m)	Angle of rotation (°)
2000—best growth	11,361.85	6,085,567.68	2609.10	742.62	71.54	1843	113.33
2021—best growth	18,102.71	23,955,235.38	3511.52	2171.63	38.16	1843	83.17
2000—good growth	13,723.44	9,388,969.56	3118.99	958.46	69.27	669	100.06
2021—good growth	18,787.67	25,619,325.36	3668.23	2223.27	39.39	669	86.43
2000—steady growth	19,977.54	29,933,138.47	3770.50	2527.15	32.98	771	77.88
2021—steady growth	19,826.47	29,571,051.25	3728.10	2524.97	32.27	771	66.64
2000—poor growth	17,614.25	23,754,641.89	3230.63	2340.65	27.55	2002	99.47
2021—poor growth	14,989.09	14,320,337.81	3168.83	1438.64	54.60	2002	88.63
2000—worst growth	15,581.28	17,855,779.39	3004.53	1891.83	37.03	3248	83.41
2021—worst growth	10,914.29	8,754,653.81	2106.16	1323.21	37.17	3248	82.94

Nearest neighbor exponential distribution

The nearest neighbor index of the spatial distribution of average vegetation with different growth from 2000 to 2021 is shown in Fig. 6 and Table 6. In the Engebei study area in the past 22 years, the distribution of vegetation with good growth was less, and its spatial distribution was relatively irregular. The average observed distance of vegetation was 763.52 m, much higher than the expected average distance of 244.71 m, and the nearest neighbor ratio was 3.12, showing a discrete spatial distribution. The nearest neighbor index of vegetation with good growth, vegetation with stable growth, vegetation with poor growth, and vegetation with poor growth is all less than 1, which reflects that the average spatial distribution pattern of 22 years is the aggregation pattern, in which vegetation with good growth is the most obvious, the average observation distance of vegetation is the shortest, the nearest neighbor ratio is 0.61, the Z score is − 9.53, and the P value is 0. It means that at the 99% confidence level, through the significance test, it indicates that the vegetation with good growth is significantly clustered and distributed in space. The average observation distance of vegetation with stable growth, poor growth, and poor growth is slightly larger, and the aggregation degree is slightly lower than that of vegetation with good growth.

Table 6

Nearest neighbor index of vegetation with different growth in Engebei in the past 21 years

Mean growth	Observed-mean distance (m)	Expected-mean distance (m)	Nearest neighbor ratio	Z-score	P	Distribution pattern
Best-growth	763.52	244.71	3.12	10.73	0.00	Discretization
Good-growth	144.55	236.55	0.61	− 9.53	0.00	Aggregation
Steady-growth	153.19	268.68	0.57	− 12.85	0.00	Aggregation
Poor-growth	151.58	229.49	0.66	− 10.95	0.00	Aggregation
Worst-growth	157.14	292.54	0.54	− 4.60	0.000004	Aggregation

Getis Ord Gi*

The vegetation vulnerability value in Engebei’s research area was analyzed, and the spatial distribution of the overall vulnerability index in 8 periods was compared and analyzed, so as to present the differences in the overall vulnerability index in different periods. The results are shown in Fig. 7 and Table 7. According to the evolution of the spatial and temporal distribution of cold hot spots, the extremely vulnerable, very safe, and extremely safe cold hot spots of the overall vulnerability index of vegetation in the Engebei research area are shrinking, from 41.72%, 3.66%, and 30.17% to 30.64%, 3.21% and 20.81%, respectively. While the very fragile, fragile, and stable cold spot area showed volatility expansion from 3.41%, 1.48%, and 17.59% to 6.55%, 3.51%, and 33.46%, the safe zone was more stable. In the study area, the cold spot area showed a decreasing trend, while the stable area and hot spot area showed an increasing trend, which indicated that the numerical clustering degree of vulnerability of the vegetation belt in the Engebei ecological area was declining continuously. From the perspective of the region, the northern part of the transition zone is still a hot spot, and the overall agglomeration characteristics are relatively stable during the study period, and the ecosystem is very safe. In the north arid steppe region, there was a great change, the concentration of safe zone was reduced, and the degree of ecosystem security decreased. In the southern desert region, the concentration area increased but the cold point value was higher and the ecological vulnerability was higher.

Table 7

Hot spot analysis of vegetation vulnerability in Engebei from 2000 to 2021

Vulnerability	2000 (%)	2003 (%)	2006 (%)	2009 (%)	2013 (%)	2015 (%)	2018 (%)	2021 (%)
Extreme safe zone	30.17	30.40	25.70	24.40	30.25	22.85	20.09	20.81
Very safety zone	3.66	5.41	4.17	5.99	5.32	3.83	3.01	3.21
Safe area	1.99	2.44	2.05	2.98	2.60	2.32	1.64	1.83
Stable region	17.59	18.18	24.50	22.83	21.98	30.80	36.41	33.46
Vulnerable zone	1.48	1.05	1.89	1.50	1.53	2.13	3.26	3.51
Very Vulnerable area	3.41	2.13	3.24	3.26	2.78	3.87	5.50	6.55
Zone of extreme vulnerability	41.72	40.39	38.47	39.04	35.54	34.20	30.08	30.64

Discussion

The vegetation change dynamic is an important index to evaluate these ecological environment changes. Long-term vegetation sequence data are often used to detect dynamic vegetation change and extract change characteristics and patterns. Normalized difference vegetation index (NDVI) is one of the important parameters to reveal the characteristics of surface vegetation growth information (Crist and Malila 1980). Teligeer et al. (2022) showed an increasing trend in a large area of NDVI in Inner Mongolia during the past 20 years from 2000 to 2019, in which the area of forest, meadow steppe, and desert steppe increased significantly in the largest proportion, while the area of the typical steppe did not increase significantly in the largest proportion, which was consistent with the results of large area improvement of vegetation growth in this paper. Monitoring vegetation growth in terrestrial ecosystems and understanding its dynamic process can be an important guide for estimating the future growth trend of vegetation in ecological areas, monitoring the changing trend of the ecological environment, understanding the internal mechanism of the role, and establishing reasonable ecological protection and supervision models.

Wan Wei et al. (2018) evaluated the regional desertification process through Landsat et al. image data and believed that on a long-time scale, there was a trend of first worsening and then reversing, which was consistent with the result that the ecologically vulnerable area of the local area transitioning to desert steppe was shrinking and developing steadily. On one hand, the ecological trend is closely related to regional climate and environment (Gu et al. 2018), on the other hand, it is closely related to regional governance measures. In the past, war damage and population increase led to indiscriminate reclamation and deforestation, which seriously deteriorated the regional ecological environment. After years of efforts in ecological area construction since the 1980s, the interference degree of vegetation growth caused by human activities has been reduced. A comprehensive shelterbelt system combining belt, net, sheet, grass, irrigation, and grass has been initially formed in EngebeiSha Area, which forms a green barrier for protecting the mother river–Yellow River, promotes the reproduction and succession process of vegetation communities. Thus, the quality of the ecological environment improves.

In terms of dynamic monitoring of vegetation growth in the study area, we can further subdivide the desert ecoregion and the arid steppe ecoregion in future research and compare the vegetation growth in the two typical areas. The main parameter in evaluating vegetation growth and ecological vulnerability is the NDVI value. Although NDVI assessment is widely used, other parameters can be added later as comparative considerations to enrich the discussion index.

Conclusions

Based on Landsat remote sensing data, the NDVI value and other information of vegetation growth in Engebei research area from 2000 to 2021 were obtained, and the spatio-temporal change characteristics, future sustainability distribution characteristics and spatial change characteristics of vegetation growth in Engebei ecological demonstration area were studied.

Interannual dynamic changes of vegetation growth in Engebei

In terms of time change, the mean NDVI of vegetation increased at a fluctuating rate of 0.0027, and the average NDVI of many years was about 0.38. The difference of NDVI decreases at a rate of -0.0021, and the positive value of the difference is greater than the negative value. The average NDVI showed a spatial distribution phenomenon of higher in the north and lower in the south, and different levels of vegetation showed different fluctuation states in the time change. Patch areas with best, good, stable, poor, and worst vegetation growth accounted for 5.27%, 6.9%, 27.08%, 60.3% and 0.46% of the total area of the study area, respectively. Over the years, the vegetation growth in Engebei research area was mainly stable and poor, and the improvement of the vegetation growth was the most obvious.

In terms of spatial distribution, the vegetation patches with poor growth are mainly distributed in the south and north of the study area, the desert areas in the south are relatively stable, and the vegetation with good growth is mainly distributed in the middle of the study area. The desert transition zone and the arid steppe zone in the central and northern part of the study area are the main areas of vegetation growth improvement, while most of the areas of vegetation growth deterioration are in the northern part of the study area.

Vegetation growth trend and sustainable distribution characteristics in Engebei

In terms of spatial trend, the vegetation growth of the growing season from 2000 to 2021 in the study area showed an overall upward trend, and the improved area accounted for about 63.44%. The change trend of vegetation growth was mainly insignificant, accounting for about 97.12%. The areas with significant vegetation degradation were mainly scattered in the northern part of the study area. The areas where vegetation growth was not significantly degraded were mainly clustered in the northern part of the study area, while a few were scattered in the southern part.

In terms of sustainable distribution, positive and negative sustainability accounted for 57.68% and 42.24%, respectively. In the future, about 23.81% of the regions will not significantly improve the anti-sustainability of vegetation growth, 38.84% of the regions will not significantly improve the sustainability of vegetation growth, the improvement area is mostly distributed in the southern desert area of the study area, and the degradation area is mostly distributed in the northern arid grassland area of the study area.

Evolution of spatial distribution pattern and ecological vulnerability of Engebei

In terms of the dynamic change of the spatial pattern of vegetation, the mean coefficient of variation of vegetation growth in the Engebei study area from 2000 to 2021 was 0.18, and vegetation growth mostly maintained a relatively high level of fluctuation during the study period, with large internal differences in the southern and northern regions, and the vegetation ecosystem around the administrative boundary was more sensitive and stable. The spatial migration and diffusion degree of vegetation is large, among which the dispersion degree of vegetation patches with good growth is the largest and the average spatial distribution in 22 years is in discrete mode, while the rest of the vegetation classes are in aggregation mode. The migration distance of vegetation with poor growth is the longest, and the development direction of vegetation patches with different growth trends gradually extends or Narrows along the southwest and northeast.

In terms of ecological vulnerability, the spatial and temporal evolution characteristics of cold hot spots showed significant differences: the ecological security characteristics of the southern desert region were enhanced, the agglomeration characteristics of the central region remained basically stable during the study period, and the ecological fragility characteristics of the northern arid steppe region were improved. During the study period in the ecological demonstration area of Engebei, the spatial distribution of vegetation of different grades is mainly clustered and the spatial distribution evolution is more and more uniform.

Declarations

Conflict of interest

Financial interests: Jie Zhang, Zhichao Yang and Shulin Zheng declare that they have no financial interests.

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Titel: Dynamic monitoring of vegetation growth in Engebei ecological demonstration area based on remote sensing
verfasst von: Jie Zhang
Zhichao Yang
Shulin Zheng
Haijun Yue
Publikationsdatum: 01.01.2024
Verlag: Springer Berlin Heidelberg
Erschienen in: Environmental Earth Sciences / Ausgabe 2/2024
Print ISSN: 1866-6280
Elektronische ISSN: 1866-6299
DOI: https://doi.org/10.1007/s12665-023-11371-7

Springer Professional

Abstract

Publisher's Note

Introduction

Materials and methods

Study area

Data source

Research method

Trend analysis

Spatial evolution analysis

Results

Vegetation growth and sustainable distribution characteristics

Evolution trend of vegetation growth

R/S analysis of vegetation growth

Future sustainability analysis of vegetation growth

Evolution of vegetation spatial distribution pattern and ecological vulnerability

Directional distribution change

Nearest neighbor exponential distribution

Getis Ord Gi*

Discussion

Conclusions

Interannual dynamic changes of vegetation growth in Engebei

Vegetation growth trend and sustainable distribution characteristics in Engebei

Evolution of spatial distribution pattern and ecological vulnerability of Engebei

Declarations

Conflict of interest

Publisher's Note

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