Land degradation process and spatial effect analysis method for centralized photovoltaic development in plateau area

Abstract

The invention discloses a land degradation process and spatial effect analysis method for centralized photovoltaic development in a plateau area. A plurality of ecological system evaluation methods such as a multi-source heterogeneous data set and an improved CASA model are adopted, a plateau surface cover change mechanism and centralized photovoltaic development characteristics are combined, and four types of land degradation processes including land cover, land productivity, desertification and vegetation conditions are selected for single-element evaluation; a comprehensive evaluation framework is constructed based on a new perspective of land degradation balance (LDN). Meanwhile, a buffer area analysis method is introduced to carry out spatial effect evaluation, and land degradation characteristics under different spatial distances are quantified. According to the method, the influence of photovoltaic power station construction on land cover and ecological environment can be comprehensively and deeply evaluated and analyzed, accurate and detailed land degradation analysis information is provided, the optimization of land management and protection strategies is facilitated, and the difficulty of a quantitative characterization method for photovoltaic development ecological environment influence and spatial heterogeneity thereof is solved.

Description

Technical Field

[0001] The present invention belongs to the field of natural resource research, and specifically relates to a land degradation process and spatial effect analysis method for centralized photovoltaic development in plateau areas. Background Art

[0002] With the adjustment, optimization and transformation and upgrading of my country's energy structure, centralized photovoltaic power stations have become increasingly prominent in sustainable energy planning and the "dual carbon" goals with their advantages of safety, economy and easy maintenance, and have maintained a high-speed expansion trend. The large-scale development of the photovoltaic industry has a significant role in promoting the construction of a clean, low-carbon, safe and efficient energy system, but its complex impact on the ecosystem cannot be ignored. The leveling and excavation of sites, soil compaction, road construction and other measures required for the construction of photovoltaic power stations will directly destroy the native vegetation and reshape the surface structure. At the same time, photovoltaic modules have the physical properties of blocking direct sunlight and airflow, and can change microclimate factors such as surface solar radiation, air temperature and humidity, wind speed and wind direction. Research based on field observations points out that in arid areas, the changes in microclimate and soil microenvironment caused by photovoltaic development are conducive to the improvement of fragile habitats, which are manifested in the form of increased vegetation coverage, diversity and productivity, and enhanced soil erosion resistance and carbon sequestration capacity. However, most of the existing studies are local observation results at the microscopic scale, and there is a lack of cross-regional comparative studies, which makes it difficult to reveal the spatial heterogeneity of the environmental impact of photovoltaic development. The introduction of satellite remote sensing technology has confirmed the positive role of photovoltaic development in ecological restoration at the photovoltaic park, desert area and even national scale. However, current research focuses on the single process analysis of vegetation changes, and no comprehensive analysis method for multi-variable and multi-dimensional ecological effects under a unified scientific assessment framework has been made public.

[0003] Therefore, in response to the limitations of insufficient spatiotemporal representation and the lack of a systematic scientific evaluation framework in the current research on the ecological effects of centralized photovoltaic development, and based on the urgent need for land degradation monitoring and management in the context of photovoltaic development, technical personnel in this field are committed to developing a method for analyzing the land degradation process and spatial effects of centralized photovoltaic development in plateau areas. Summary of the invention

[0004] This paper combines multi-source heterogeneous data sets with improved CASA models, desertification remote sensing simulation monitoring models, pixel dichotomy models and other methods, and builds a comprehensive assessment framework covering four types of land degradation processes based on the new perspective of LDN. Through systematic analysis of land degradation before and after the construction and development of photovoltaic clusters, from element analysis to integrated assessment, the overall ecological impact and spatial effects that vary with distance are deeply explored.

[0005] In order to achieve the above object, the present invention is implemented by adopting the following technical scheme: the method comprises:

[0006] Construct an evaluation framework based on multi-source heterogeneous spatiotemporal data;

[0007] Based on the single factor assessment results of four types of land degradation processes, namely land cover, land productivity, desertification and vegetation status, an integrated land degradation assessment is carried out in accordance with the 1OAO principle;

[0008] The spatial effect assessment of the land degradation process is based on the change in the difference in the proportion of net land restoration area between the baseline period and the assessment period over spatial distance.

[0009] In one embodiment, the assessment framework includes:

[0010] (1) Delineation of the temporal and spatial scope of land degradation assessment;

[0011] (2) Single factor assessment of land degradation;

[0012] (3) Integrated assessment of land degradation;

[0013] (4) Analysis of spatial effects of the evaluation framework.

[0014] In one scheme, the method for single-factor assessment of land degradation process includes: the land cover assessment method adopts China's multi-period land use remote sensing monitoring dataset, and reclassifies it into seven types of land cover, namely forest land, grassland, cultivated land, wetland, artificial surface, others and water bodies, as the assessment data source.

[0015] In one scheme, the method for single factor assessment of land degradation process includes: the land productivity assessment method adopts the improved CASA model, the formula is:

[0016] NPP(x,t)=APAR(x,t)×ε(x,t)

[0017] Where APAR(x,t) represents the photosynthetically active radiation absorbed by pixel x in month t (gC / m 2 ), ε(x,t) represents the actual light energy utilization rate of pixel x in month t (gC·MJ).

[0018] In one scheme, the method for single-factor assessment of land degradation process includes: desertification assessment uses a remote sensing simulation monitoring model for desertification applicable to the Qinghai-Tibet Plateau, and the formula is:

[0019] DDI=2.02×NDVI+1.32×Wet-Albedo

[0020] Wherein, DDI is the desertification difference index, the smaller the value, the more serious the desertification; NDVI is the normalized difference vegetation index; Wet is the remote sensing tasseled cap transform wetness index; Albedo is the surface reflectance.

[0021] In one scheme, the single-factor assessment method of the land degradation process includes: the vegetation condition assessment uses the fractional vegetation coverage (FVC) as a characterization indicator and uses a pixel binary model for estimation, and the formula is:

[0022]

[0023] Where, NDVI is the normalized difference vegetation index; max and NDVI min They are the NDVI at cumulative frequencies of 95% and 5%, respectively.

[0024] In one scenario, the spatial effect analysis adopts a buffer zone analysis method, creating multiple buffer zones from the vector boundary of the photovoltaic array outward, with the width increasing in steps of 30m until it reaches 180m, and using the "net restored area ratio" indicator to quantify the land degradation characteristics at different spatial distances;

[0025]

[0026] Where P NR A is the percentage of net restored area; R , A D and A Total Represent the restored and degraded land areas and the total area of ​​the region respectively; when P NR When ≥0, it means that the regional status meets the core goal of the LDN framework of “offsetting degradation through restoration”; otherwise, it means that the regional land status is unbalanced and PV development has aggravated the degradation pressure of the land system.

[0027] Beneficial effects of the present invention:

[0028] The method of the present invention combines multi-source heterogeneous data sets with multiple ecological models, and constructs a comprehensive assessment framework based on the new perspective of LDN, which can comprehensively assess and analyze the degree of land system degradation, and reveal the characteristics of land degradation at different spatial distances through spatial effect assessment. This method can not only provide more accurate and comprehensive land degradation analysis information, which helps to optimize land management and protection strategies, but also helps to analyze the spatial heterogeneity of the impact of photovoltaic development on the environment, and provide a scientific basis for promoting more sustainable green energy development methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 The land degradation assessment framework under the impact of photovoltaic development designed for this invention;

[0030] Figure 2 The results of land cover change assessment for photovoltaic industry clusters;

[0031] Figure 3 The relationship between land cover status transfer before and after photovoltaic development in photovoltaic industry clusters;

[0032] Figure 4 The results of the land productivity change assessment for the three photovoltaic industry clusters;

[0033] Figure 5 The relationship between the land productivity status transfer before and after photovoltaic development of photovoltaic industry clusters;

[0034] Figure 6 The results of desertification change assessment for large photovoltaic industrial clusters;

[0035] Figure 7 The relationship between desertification status transfer before and after photovoltaic development of photovoltaic industry clusters;

[0036] Figure 8 The results of the assessment of changes in vegetation conditions in the photovoltaic industry cluster;

[0037] Fig. 9 The relationship between the vegetation status transfer before and after photovoltaic development in photovoltaic industry clusters;

[0038] Fig.10 Integrate assessment results of land degradation for photovoltaic industrial clusters;

[0039] Fig.11 The transfer relationship of land degradation status before and after photovoltaic development in photovoltaic industry clusters and the contribution rate of each indicator;

[0040] Fig.12 This is the relationship between the proportion of net restored land area of ​​photovoltaic industry clusters and spatial distance. DETAILED DESCRIPTION

[0041] Unless otherwise defined, all technical and scientific terms used in the present invention have the same meaning as those commonly understood by technicians in the technical field of the present invention. The terms used in the specification of the present invention are intended to describe specific embodiments rather than to limit the scope of application of the present invention. To facilitate understanding of the present invention, the present invention will be described more fully below in conjunction with the relevant drawings. Typical embodiments of the present invention are shown in the drawings. However, the present invention has a variety of application forms and is not limited to the embodiments. On the contrary, the embodiments are provided to make the disclosure of the present invention more thorough and detailed.

[0042] like Figure 1 As shown in the figure, the land degradation process and spatial effect analysis methods of centralized photovoltaic development in plateau areas include:

[0043] Introducing a new perspective of "Land Degradation Neutrality" (LDN), aiming at the micro-scale characteristics of the high-cold and arid environment in plateau areas and the environmental impact of photovoltaic development, an evaluation framework based on multi-source heterogeneous spatiotemporal data is constructed, including the following main steps:

[0044] S1. Delineation of the temporal and spatial scope of land degradation assessment. Since centralized photovoltaic development in my country began around 2010, the assessment period is divided into two stages: 2000-2010 is the baseline period, which represents the land degradation process when it is not affected by photovoltaic development; 2010-2020 is the assessment period, which is used to analyze the response of land degradation to photovoltaic development. The analysis range is set as the area 180m around the photovoltaic array, and the rationality of this range has been confirmed in related studies.

[0045] S2. Single-element assessment of land degradation. The original three elements of land cover, land productivity and soil organic carbon in the LDN framework are improved and optimized. Desertification can reflect the loss of soil organic carbon to a certain extent, and it is also one of the most significant manifestations of land degradation in arid and semi-arid areas. Therefore, the desertification element replaces soil organic carbon. In addition, given that relevant studies have shown that photovoltaic development has a significant impact on vegetation conditions, the vegetation status element is included in the framework. Among them, the correspondence between land cover change and land status refers to the "SDG 15.3.1 Good Practice Guide for Assessment", and is determined by the land cover transfer matrix generated by the baseline period and the start and end years of the assessment period. For the indicators of land productivity, desertification, and vegetation status, the Theil-Sen Median slope estimation and Mann-Kendall test method are combined to evaluate the trend of long-term data changes. If the trend is not significant, it is considered stable; if it is an upward trend, it is recovery, otherwise it is considered degradation.

[0046] (1) Land cover assessment method

[0047] Photovoltaic development will change the way land is used, usually involving large-scale conversion of land to photovoltaic arrays, which in turn affects the use function and ecological status of the surrounding land. This application uses the China Multi-period Land Use Remote Sensing Monitoring Dataset (CNLUCC) and reclassifies it into seven types of land cover: forest land, grassland, cultivated land, wetland, artificial surface, others and water bodies as the evaluation data source.

[0048] (2) Land productivity assessment method

[0049] Land productivity represents the biological production capacity of land, reflecting the long-term changes in land health and production capacity. It is often expressed as vegetation net primary productivity (NPP). This application is estimated based on the improved CASA model, and the formula is:

[0050] NPP(x,t)=APAR(x,t)×ε(x,t)

[0051] Where APAR(x,t) represents the photosynthetically active radiation absorbed by pixel x in month t (gC / m 2);ε(x,t) represents the actual light energy utilization rate of pixel x in month t (gC·MJ). For details on parameter values ​​and calculation methods, please refer to the literature.

[0052] (3) Desertification Assessment

[0053] Desertification is manifested in soil erosion, sand expansion and other phenomena, and is a typical land degradation process in arid and semi-arid areas. This application uses a remote sensing simulation monitoring model for desertification on the Qinghai-Tibet Plateau, and the formula is:

[0054] DDI=2.02×NDVI+1.32×Wet-Albedo

[0055] Wherein, DDI is the desertification difference index, the smaller the value, the more serious the desertification; NDVI is the normalized difference vegetation index; Wet is the remote sensing tasseled cap transform wetness index; Albedo is the surface reflectance.

[0056] (4) Vegetation status assessment

[0057] As a key factor in the land degradation process, vegetation status has been confirmed by relevant studies to have a complex response to photovoltaic development. This application uses fractional vegetation cover (FVC) to characterize vegetation status and uses a pixel binary model for estimation. The formula is:

[0058]

[0059] Where, NDVI is the normalized difference vegetation index; max and NDVI min They are the NDVI at cumulative frequencies of 95% and 5%, respectively.

[0060] S3. Integrated assessment of land degradation. The integrated assessment of land degradation balance follows the One out All out (1OAO) principle: if a pixel is in a degraded state in any indicator, the comprehensive state of the land is considered to be degraded; if all indicators are stable, the comprehensive state is considered to be stable; if the indicators are both stable and restored, the comprehensive state is restored. Evaluation based on this principle not only makes the results more rigorous, but also helps to eliminate or reduce the impact of collinearity or non-independence problems that may exist in indicator selection. S4. Spatial effect analysis. The impact mechanism of photovoltaic development on the ecological environment can be attributed from the natural and human levels, such as microclimate change, changes in soil microenvironment, or washing of photovoltaic panels to increase soil moisture. These effects usually vary with the spatial distance to the photovoltaic array, showing significant distance dependence. In order to analyze its action law, this application adopts a buffer analysis method to create multiple buffers from the vector boundary of the photovoltaic array outward, with the width increasing in steps of 30m until it reaches 180m. Given that the analysis involves multiple factors, in order to ensure the comparability of indicators, the "net restored area share" indicator is used to quantify land degradation characteristics at different spatial distances.

[0061]

[0062] Where P NR A is the percentage of net restored area; R , A D and A Total Represent the restored and degraded land areas and the total area of ​​the region respectively. NR When ≥0, it means that the regional status meets the core goal of the LDN framework of “offsetting degradation through restoration”; otherwise, it means that the regional land status is unbalanced and PV development has aggravated the degradation pressure of the land system.

[0063] Example:

[0064] This embodiment uses the three major photovoltaic industrial clusters in Qinghai Province as case areas, namely, Golmud East Exit Photovoltaic Industrial Park, Delingha Photovoltaic Thermal Industrial Park and Taratan Photovoltaic Industrial Park (hereinafter referred to as Golmud, Delingha and Taratan respectively). The photovoltaic distribution data used comes from the Chinese Photovoltaic Power Station Spatial Range Dataset released by the research team of Associate Professor Chen Yuehong of Hohai University. This dataset not only performs well in four indicators: recall rate (95.12%), precision (97.07%), F1 score (96.08%) and intersection ratio (92.46%), but also includes photovoltaic construction time. Considering that photovoltaic clusters are relatively small spatial units, the data in the study area were further manually revised based on the visual interpretation of remote sensing images. Since the potential impact of photovoltaic panel occlusion on remote sensing indices is not yet clear, this embodiment does not analyze the photovoltaic array coverage area for the time being.

[0065] The main data types used in the land degradation index assessment include: ① remote sensing data, using Landsat 5 / 7 / 8 remote sensing images provided by the GEE cloud platform, with dataset numbers LANDSAT / LT05 / C02 / T1_L2, LANDSAT / LE07 / C02 / T1_L2 and LANDSAT / LC08 / C02 / T1_L2 respectively, and preprocessing such as cloud removal and study area cropping was completed on GEE; ② land use data, from the China Land Cover Dataset (CLCD); ③ meteorological data, including monthly average temperature and precipitation, from the National Tibetan Plateau Data Center (http: / / data.tpdc.ac.cn); solar radiation data from the TerraClimate dataset, obtained through GEE; ④ vegetation type data, using the 30m resolution vegetation map of the Qinghai-Tibet Plateau from the National Tibetan Plateau Data Center. The above raster data were estimated after being uniformly resampled to 30m resolution under the Krasovsky_1940_Albers map projection.

[0066] Land Cover Change Assessment:

[0067] The land cover change assessment results of this embodiment are as follows: Figure 2 As shown in the figure. Overall, the land cover status of the three major photovoltaic industrial clusters in Qinghai has changed relatively limitedly, mainly showing stability. In the baseline period, the distribution of restoration and degradation patches in the Golmud area was sparse and random, mainly from the conversion between low-coverage grassland and sandy land, indicating that the natural succession process in the region at this stage still dominated under the influence of weak human activities. There was a concentrated area of ​​degradation and restoration in Delingha and Tara Beach, respectively. These changes were caused by the occupation of grassland by farmland reclamation and the restoration of sandy land to grassland. Entering the evaluation period, the degradation and restoration patches in Golmud and Delingha showed significant linear distribution characteristics, reflecting the increase in the intensity of human activities caused by photovoltaic development, and showing a certain spatial distribution law. In contrast, the changes in Tara Beach were relatively weak, and there was no obvious spatial distribution characteristics, which may reflect that the intensity of human activities associated with photovoltaic development on land cover changes in this area is relatively low.

[0068] The land cover state transition relationship before and after photovoltaic development evaluated in this example is as follows Figure 3As shown. Within the scope of analysis, from the baseline period to the assessment period, the proportion of restored area in Golmud and Delingha increased slightly, by 0.78% and 2.09% respectively, while the proportion of restored area in Tara Beach decreased by 0.46%, and the degradation phenomenon intensified. The stable state is still absolutely dominant, and the proportion of stable areas in the three clusters exceeds 97.45%. In arid and semi-arid areas, the site selection of centralized photovoltaic development tends to be in areas with weak disturbance from human activities, and the land cover type is relatively single, mainly grassland and unused land. Therefore, except for the land area occupied by the photovoltaic array itself and small-scale photovoltaic supporting facilities, the overall change of photovoltaic development on regional land cover conditions is relatively small and the impact is relatively limited. Assessment of land productivity changes:

[0069] The land productivity change assessment results of this example are as follows: Figure 4 As shown in Figure 2 . The spatial distribution of land productivity in the three clusters differed significantly between the baseline and evaluation periods. In the baseline period, land productivity in Golmud and Taratan was mainly restored, while degradation in Delingha mainly occurred in the farmland area to the south. In the evaluation period, all three clusters showed obvious degradation patches. The original farmland degradation in Delingha was reversed and became stable or even restored, but new degradation patches appeared on the north side of the photovoltaic area. It is worth noting that there is a significant degradation area in Golmud that overlaps with the photovoltaic array, and the land degradation phenomenon is also obvious on the north side of the photovoltaic in the west of Taratan. The construction time of the photovoltaic arrays adjacent to these degraded areas was close to the end of the evaluation period, and the sudden drop in NPP caused by the construction may be the direct cause of the degradation. On the contrary, the land status in the surrounding areas of other photovoltaic arrays with earlier construction time is generally stable or even restored, which to a certain extent shows that the interference of photovoltaic construction on land productivity is mainly short-term and difficult to transform into a long-term trend.

[0070] The relationship between the land productivity state transition before and after photovoltaic development evaluated in this embodiment is as follows: Figure 5 As shown. Within the scope of analysis, the three major clusters had almost no degraded area in the baseline period, but the proportion of degraded area increased in the assessment period, which was mainly due to the transformation of the stable land state. This phenomenon reflects the inevitability of local degradation during the photovoltaic development process. The degraded area in Delingha increased the most, rising to 3.84%. The proportion of degraded areas in Golmud and Taratan increased to 0.82% and 1.97% respectively. Despite some degradation, stability is still the absolute dominant state of land productivity changes. Even in Delingha, where the transformation was relatively drastic, the proportion of stable areas in the assessment period was still as high as 94.59%.

[0071] Desertification change assessment:

[0072] The desertification change assessment results of this example are as follows: Figure 6As shown. Among all the elements of land degradation process, desertification recovery is the most significant. From the baseline period to the assessment period, it can be observed that photovoltaic development and other human activities have shaped the change of desertification status. The greening effect brought by the construction of roads inside and around the Golmud photovoltaic park is obvious, which effectively curbs the process of desertification. Most of the Delingha area is restored. However, the photovoltaic park and its north side are affected by photovoltaic development, and the recovery state of the baseline period has not been continued. The most concentrated and continuous stable patches of Tara Beach during the assessment period appeared near the latest photovoltaic array built on the west side. However, the photovoltaic park and its north side are affected by photovoltaic development, resulting in part of the area failing to continue the recovery state of the baseline period. The recovery area of ​​Tara Beach extends significantly westward from the coast of Longyangxia Reservoir on the east side, but there is an abnormally stable area near the latest photovoltaic array built on the west side.

[0073] The relationship between the land productivity state transition before and after photovoltaic development evaluated in this embodiment is as follows: Figure 7 As shown. Within the analysis range, the data of both periods show that the dominant state of Golmud is stable, with the proportion increasing from 64.28% to 70.98%, while the dominant state of Delingha and Tara Beach is recovery, accounting for 68.58% and 72.66% of the evaluation period respectively. After the construction of photovoltaic power stations, the recovery area of ​​Golmud and Delingha decreased, and the stable area increased; in contrast, the recovery area of ​​Tara Beach increased, and nearly half of it came from the transformation of stable areas. The desertification status of the three major clusters has undergone significant changes, and the area exchange between stable and recovery states is significant.

[0074] Assessment of changes in vegetation conditions:

[0075] The vegetation status change assessment results of this embodiment are as follows: Figure 8 As shown in the figure. The shaping effect of photovoltaic development and other human activities on the assessment results of vegetation status changes is different in different photovoltaic clusters. This feature is most fully reflected in Golmud, where vegetation restoration in and around the photovoltaic cluster almost completely overlaps with the road construction area. After photovoltaic development in Delingha, the restoration patches near the photovoltaic cluster decreased sharply and even degraded. However, it is difficult to observe significant human activity impacts in Tara Beach.

[0076] The relationship between the land productivity state transition before and after photovoltaic development evaluated in this embodiment is as follows: Fig. 9As shown. Within the scope of analysis, the three major clusters all experienced a shift from the baseline period to the assessment period with a decrease in restored area and an increase in degraded area. The most dramatic change was in Delingha, where the degraded area increased from almost zero in the baseline period to 3.84%, while the proportion of restored area decreased from 19.33% to 3.55%, resulting in a higher degraded area than restored area in the assessment period. This shows that the impact of photovoltaic development on vegetation conditions is relatively complex, especially in local areas, which may increase the risk of land degradation, and if effective ecological protection measures are not taken, it may have a long-term adverse impact on the sustainability of the land.

[0077] Integrated Land Degradation Assessment:

[0078] The integrated land degradation assessment results of this example are as follows: Fig.10 As shown. Based on the single-factor assessment results of four types of land degradation processes, namely land cover, land productivity, desertification and vegetation status, an integrated land degradation assessment was conducted according to the 1OAO principle. The integrated assessment results show that from the baseline period to the assessment period, driven by human activities such as photovoltaic development, the spatial distribution of land degradation status has changed significantly, especially in the areas surrounding the photovoltaic arrays that were built later, the land recovery status in the baseline period could not be continued, and even land degradation occurred. The land degradation assessment framework that incorporates multiple factors reveals that the impact of centralized photovoltaic development on the land system is more complex.

[0079] The transition relationship of land degradation status before and after photovoltaic development and the contribution rate of each indicator evaluated in this example are as follows: Fig.11 As shown. Within the analysis range, the proportion of degraded area in the three clusters increased compared with the baseline period, among which the degraded area in Delingha increased the most, reaching 7.56%. It is worth noting that 83.95% of the degraded area in Delingha was converted from the restored state in the baseline period, which is more unfavorable to the realization of LDN goals. The restored area in Golmud and Delingha has decreased to a certain extent, while that in Tara Beach has increased. The differences among the three clusters are mainly reflected in the proportion of restoration status conversion: Golmud has the highest proportion of original restored area transferred out, reaching 73.08%, and the area converted from stable to restored has also reached 44.59% of the original restored area, which is still insufficient overall; in Delingha, the proportion of original restored area transferred out is 34.55%, while the restored area transferred in only accounts for 18.15% of the original restored area; the proportion of restored area transferred out in Tara Beach is 33.24%, while the transferred in area is relatively large, reaching 57.41% of the original restored area. The heterogeneity of ecological and environmental impacts of photovoltaic development in the three major clusters is revealed, and corresponding ecological management and restoration strategies need to be formulated according to the actual conditions in each region.

[0080] From the perspective of contribution to the integrated assessment, the degradation area dominated by changes in vegetation conditions under the influence of photovoltaic development accounts for the largest proportion in the three clusters, with an average of 2.89% for the three clusters, of which Delingha has the highest proportion of degraded area, reaching 4.11%. Although the natural environment background and development intensity of the three clusters are different, the vegetation condition is the element of land degradation process most seriously affected by photovoltaic development. This finding highlights the need to include vegetation conditions in the assessment framework. The restoration area dominated by desertification changes accounts for the largest proportion, with an average of 46.49% for the three clusters, and the highest in Delingha is 59.36%. Previous studies have shown that photovoltaic clusters can change the relationship between albedo, precipitation and vegetation, forming a positive feedback mechanism, which may be one of the most significant reasons for desertification recovery.

[0081] Spatial effect assessment of photovoltaic development on land degradation processes:

[0082] The integrated land degradation assessment results of this example are as follows: Fig.12 As shown in the figure, the distance differentiation of land degradation process elements and integrated assessment net restoration area before and after photovoltaic development is depicted. In the baseline period, the change of net restoration area of ​​the three clusters does not change with the increase of distance, indicating that the land degradation process at this stage is mainly affected by changes in natural conditions such as climate. Therefore, the change of the absolute value of the difference between the baseline period and the assessment period with spatial distance can effectively reveal the spatial effect of photovoltaic development on the land degradation process.

[0083] During the assessment period, the land cover recovery in Golmud and Delingha was higher than the baseline period, while that in Taratan was lower than the baseline period. Although the net recovery area of ​​the three clusters was greater than zero in general, this value was relatively lower than other factors, with the highest value not exceeding 2.85%, and the distance differentiation did not show obvious regularity.

[0084] In terms of land productivity, the net recovery area of ​​the three clusters was lower than that of the baseline period. Golmud and Delingha showed an overall trend of increasing net recovery area with increasing distance. Among them, Golmud achieved a balance of productivity changes within a range of about 120m, and the changes tended to be stable beyond this distance; Delingha reached the maximum net degradation area within 60m and failed to achieve balance within the analysis range. In contrast, the spatial effect of Tara Beach was not obvious, and its net recovery area accounted for about -1.62%.

[0085] The proportion of net desertification recovery area varies significantly among the three clusters. The recovery area within 30m in Golmud and Delingha is basically the same as that in the baseline period. As the distance increases, the proportion of net recovery area gradually decreases and the rate of change tends to slow down. However, Tara Beach fails to show obvious spatial effects. The net recovery area of ​​Delingha and Tara Beach can reach 80%, while the highest value in Golmud is only 40%, a difference of more than double. This difference needs to be analyzed in combination with the relative position of the assessment period and the baseline period. Only the recovery area of ​​Tara Beach in the assessment period is higher than that in the baseline period, highlighting the positive role of photovoltaic development in desertification prevention and control in this region.

[0086] In terms of vegetation conditions, the net recovery area percentage of the three clusters is lower than that of the baseline period. In the baseline period, Delingha had the largest net recovery area percentage. However, Delingha is still the only photovoltaic industry cluster that has not achieved a balance in vegetation condition changes during the assessment period, which further confirms that the vegetation condition in Deling is most significantly affected by photovoltaic development. The three clusters all show a trend of increasing net recovery area percentage with increasing distance. After the range exceeds 120m, the change in Golmud tends to be flat, fluctuating around 2.65%, which may indicate that this distance is the spatial threshold for the impact of photovoltaic development on local vegetation.

[0087] From the results of the integrated assessment, Golmud has the strongest spatial effect, showing a trend of gradually decreasing net restoration area with increasing distance. The difference in the proportion of net restoration area within 30m and 180m is 2.51%, indicating that photovoltaic development may have begun to play a positive effect concentrated near the photovoltaic array, but has not yet completely offset the short-term disturbance caused by the construction period. Therefore, long-term continuous monitoring is still needed to further evaluate its impact. The spatial effect at the element level in Delingha did not appear in the integrated assessment results, which to a certain extent shows that the assessment framework has strong stability and anti-interference ability, and can more objectively reflect the overall trend of land degradation process with spatial distance, rather than being limited to the change characteristics of a single element.

[0088] Among the three major clusters, only Taratan's net restored area during the assessment period was higher than the baseline period, with an increase of 8.65%, indicating that only Taratan's photovoltaic development has a positive effect on land degradation. This may be due to the fact that the development scale of the Taratan photovoltaic cluster is among the highest in the world, which is more conducive to the full realization of positive ecological benefits. It may also be related to the unique geographical characteristics of the region where it is located, such as the nearby Longyangxia Reservoir, which has an ecological regulation function. The overall proportion of net restored area in Delingha during the assessment period decreased by 20.13% compared with the baseline period. This decline deserves great attention. As the only area among the three major photovoltaic clusters with large-scale agricultural planting activities nearby, Delingha's land system carries a higher frequency of human activities and faces stronger ecological disturbances.

[0089] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by computer program instructions and related hardware, and the program can be stored in a computer-readable storage medium and implement the processes in the embodiments when executed. The storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM).

[0090] It should be understood that the detailed description of the technical solution of the present invention with the help of the preferred embodiments is illustrative rather than restrictive. A person skilled in the art can, based on reading the specification of the present invention, appropriately modify the technical solution in the embodiments, or perform equivalent replacement of some technical features without departing from the core idea and technical scope of the present invention, and the above modifications or replacements should all be within the scope of protection determined by the claims.

Claims

1. The land degradation process and spatial effect analysis method of centralized photovoltaic development in plateau areas is characterized by: The method includes: Construct an evaluation framework based on multi-source heterogeneous spatiotemporal data; Based on the single factor assessment results of four types of land degradation processes, namely land cover, land productivity, desertification and vegetation status, an integrated land degradation assessment is carried out in accordance with the 1OAO principle; The spatial effect assessment of the land degradation process is based on the change in the difference in the proportion of net land restoration area between the baseline period and the assessment period over spatial distance.

2. The land degradation process and spatial effect analysis method for centralized photovoltaic development in plateau areas according to claim 1, characterized in that: The assessment framework described includes: (1) Delineation of the temporal and spatial scope of land degradation assessment; (2) Single factor assessment of land degradation; (3) Integrated assessment of land degradation; (4) Analysis of spatial effects of the evaluation framework.

3. The land degradation process and spatial effect analysis method for centralized photovoltaic development in plateau areas according to claim 2, characterized in that: The single-factor assessment method for the land degradation process includes: the land cover assessment method uses China's multi-period land use remote sensing monitoring dataset, and reclassifies it into seven types of land cover, including forest land, grassland, cultivated land, wetland, artificial surface, others and water bodies, as the assessment data source.

4. The land degradation process and spatial effect analysis method for centralized photovoltaic development in plateau areas according to claim 2, characterized in that: The single factor assessment method of the land degradation process includes: the land productivity assessment method adopts the improved CASA model, the formula is: NPP(x,t)=APAR(x,t)×ε(x,t) Where APAR(x,t) represents the photosynthetically active radiation absorbed by pixel x in month t (gC / m 2 ), ε(x,t) represents the actual light energy utilization rate of pixel x in month t (gC·MJ).

5. The land degradation process and spatial effect analysis method for centralized photovoltaic development in plateau areas according to claim 2, characterized in that: The single-factor assessment method of the land degradation process includes: desertification assessment uses a remote sensing simulation monitoring model for desertification applicable to the Qinghai-Tibet Plateau, and the formula is: DDI=2.02×NDVI+1.32×Wet-Albedo Wherein, DDI is the desertification difference index, the smaller the value, the more serious the desertification; NDVI is the normalized difference vegetation index; Wet is the remote sensing tasseled cap transform wetness index; Albedo is the surface reflectance.

6. The land degradation process and spatial effect analysis method for centralized photovoltaic development in plateau areas according to claim 2, characterized in that: The single-factor assessment method of the land degradation process includes: the vegetation condition assessment uses the fractional vegetation coverage (FVC) as a characterization indicator and uses the pixel binary model for estimation, and the formula is: Where, NDVI is the normalized difference vegetation index; max and NDVI min They are the NDVI at cumulative frequencies of 95% and 5%, respectively.

7. The land degradation process and spatial effect analysis method for centralized photovoltaic development in plateau areas according to claim 2, characterized in that: The spatial effect analysis adopts the buffer zone analysis method, creating multiple buffer zones from the vector boundary of the photovoltaic array outward, with the width increasing in steps of 30m until it reaches 180m, and using the "net restored area ratio" indicator to quantify the land degradation characteristics at different spatial distances; Where P NR A is the percentage of net restored area; R , A D and A Total Represent the restored and degraded land areas and the total area of ​​the region respectively; when P NR When ≥0, it means that the regional status meets the core goal of the LDN framework of "offsetting degradation through restoration". Otherwise, it means that the regional land status is unbalanced and photovoltaic development has aggravated the degradation pressure of the land system.

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