Fractional Vegetation Cover Research Articles

Evaluation of the Evolution of the Ecological Security of Oases in Arid Regions and Its Driving Forces: A Case Study of Ejina Oasis in China

Ecological security is an important guarantee of human security and survival, closely related to sustainable development. However, the ecological security evaluation and driving force analysis of oases in arid areas is still insufficient. Ejina Oasis’s ecological security has experienced significant shifts following the centralized management of the Heihe River’s water allocation. Understanding the shifts in ecological security in the Ejina region is paramount for the oasis’s long-term sustainability. This paper employed the Pressure–State–Response (P–S–R) model to select socioeconomic and ecological indicators, establish a comprehensive ecological security evaluation index system, and then analyze the evolving ecological security in the region. Additionally, this paper explored the relationship between changes in the water area, oasis area, and ecological security by using the gray correlation degree to quantify the influence of Land Use Changes (LUCCs) on the overall ecological security. From 2000 to 2012, the ecological security index of Ejina Oasis remained relatively stable at around 0.4. However, a noticeable upward trend in the ecological security index emerged from 2012 to 2020, indicating a shift toward improved ecological security in the region. Critical determinants of this change included the habitat degradation degree, total population, habitat quality, carbon stock, fractional vegetation cover (FVC), the proportion of tertiary industry, and the volume of water discharged from Wolf Heart Mountain. The degree of habitat degradation, fractional vegetation cover (FVC), and the proportion of tertiary industry had the greatest impact on the change in ecological security. The pressure index was dominant in influencing ecological security before 2012 but gradually transitioned to the state index. This study offers a valuable framework for assessing the intricate relationship between LUCCs and ecological security in water-scarce, arid-region oases.

Image-based vegetation analysis of desertified area by using a combination of ImageJ and Photoshop software.

Fractional vegetation cover (FVC) is a crucial indicator to estimate degradation and desertification for grasslands. However, traditional small-scale FVC analysis methods, such as visual estimation and point-sampling, are cumbersome and imprecise. Innovative methods like image-based FVC analysis methods, while accurate, face challenges such as complex analytical procedures and the necessary training for operations. Therefore, in this study, a combined application of ImageJ and Photoshop was employed to achieve a more effective analysis of FVC values in desertification areas. Our results showed that the FVC results obtained by combination of Photoshop and ImageJ were dependable and precise (R2 > 0.98), demonstrating equivalency to results obtained through either visual estimation or Photoshop-based methods. Furthermore, even in the face of background interference and varied shooting angles, the combination of ImageJ and Photoshop software was still able to maintain a low error rate when analyzing FVC values (average error rate = - 2.6%). In conclusion, the imaged-based combined FVC analysis method employed in our research was an effective, precise, and efficient technique for analyzing small-scale FVC, promising substantial improvement over conventional methods.

Pretrained Deep Learning Networks and Multispectral Imagery Enhance Maize LCC, FVC, and Maturity Estimation

Crop leaf chlorophyll content (LCC) and fractional vegetation cover (FVC) are crucial indicators for assessing crop health, growth development, and maturity. In contrast to the traditional manual collection of crop trait parameters, unmanned aerial vehicle (UAV) technology rapidly generates LCC and FVC maps for breeding materials, facilitating prompt assessments of maturity information. This study addresses the following research questions: (1) Can image features based on pretrained deep learning networks and ensemble learning enhance the estimation of remote sensing LCC and FVC? (2) Can the proposed adaptive normal maturity detection (ANMD) algorithm effectively monitor maize maturity based on LCC and FVC maps? We conducted the following tasks: (1) Seven phases (tassel initiation to maturity) of maize canopy orthoimages and corresponding ground-truth data for LCC and six phases of FVC using UAVs were collected. (2) Three features, namely vegetation indices (VI), texture features (TF) based on Gray Level Co-occurrence Matrix, and deep features (DF), were evaluated for LCC and FVC estimation. Moreover, the potential of four single-machine learning models and three ensemble models for LCC and FVC estimation was evaluated. (3) The estimated LCC and FVC were combined with the proposed ANMD to monitor maize maturity. The research findings indicate that (1) image features extracted from pretrained deep learning networks more accurately describe crop canopy structure information, effectively eliminating saturation effects and enhancing LCC and FVC estimation accuracy. (2) Ensemble models outperform single-machine learning models in estimating LCC and FVC, providing greater precision. Remarkably, the stacking + DF strategy achieved optimal performance in estimating LCC (coefficient of determination (R2): 0.930; root mean square error (RMSE): 3.974; average absolute error (MAE): 3.096); and FVC (R2: 0.716; RMSE: 0.057; and MAE: 0.044). (3) The proposed ANMD algorithm combined with LCC and FVC maps can be used to effectively monitor maize maturity. Establishing the maturity threshold for LCC based on the wax ripening period (P5) and successfully applying it to the wax ripening-mature period (P5–P7) achieved high monitoring accuracy (overall accuracy (OA): 0.9625–0.9875; user’s accuracy: 0.9583–0.9933; and producer’s accuracy: 0.9634–1). Similarly, utilizing the ANMD algorithm with FVC also attained elevated monitoring accuracy during P5–P7 (OA: 0.9125–0.9750; UA: 0.878–0.9778; and PA: 0.9362–0.9934). This study offers robust insights for future agricultural production and breeding, offering valuable insights for the further exploration of crop monitoring technologies and methodologies.

Effects of precipitation changes on fractional vegetation cover in the Jinghe River basin from 1998 to 2019

Studying the spatiotemporal evolutionary characteristics of vegetation and the effect of precipitation changes is necessary for understanding the regional ecological environment. We used trend analysis, partial correlation analysis, significance tests, and residual trend analysis to analyze the spatiotemporal evolution characteristics and driving factors of fractional vegetation cover (FVC) in the Jinghe River Basin (JRB) from 1998 to 2019. The results showed that vegetation coverage in the JRB significantly improved from 1998 to 2019. FVC showed an increasing trend in 90.64% of the areas in the JRB, and the overall annual change in FVC was extremely significant (p ≤ 0.01). However, precipitation in the JRB showed an insignificant increasing trend; the annual distribution of precipitation developed in a uniform direction and the precipitation centroid tended to move backward. The area with a significant correlation between the FVC change and the precipitation concentration index accounted for the largest proportion (18.47%). Precipitation changes in the JRB generally favored vegetation recovery; however, the effect was limited and non-precipitation factors dominated the changes in FVC. Our study contributes to a more comprehensive understanding of the effects of changes in precipitation patterns on vegetation cover to facilitate regional ecological protection.

Characteristics and Drivers of Vegetation Change in Xinjiang, 2000–2020

Examining the features of vegetation change and analyzing its driving forces across an extensive time series in Xinjiang are pivotal for the ecological environment. This research can offer a crucial point of reference for regional ecological conservation endeavors. We calculated the fractional vegetation cover (FVC) using MOD13Q1 data accessed through the Google Earth Engine (GEE) platform. To discern the characteristics of vegetation changes and forecast future trends, we employed time series analysis, coefficient of variation, and the Hurst exponent. The correlation between climate factors and FVC was investigated through correlation analysis. Simultaneously, to determine the relative impact of meteorological change and anthropogenic actions on FVC, we utilized multiple regression residual analysis. Furthermore, adhering to China’s ecological functional zone classification, Xinjiang was segmented into five ecological zones: R1 Altai Mountains-Junggar West Mountain Forest and Grassland Ecoregion, R2 Junggar Basin Desert Ecoregion, R3 Tianshan Mountains Mountain Forest and Grassland Ecoregion, R4 Tarim Basin-Eastern Frontier Desert Ecoregion, and R5 Pamir-Kunlun Mountains-Altan Mountains Alpine Desert and Grassland Ecoregion. A comparative analysis of these five regions was subsequently conducted. The results showed the following: (1) During the first two decades of the 21st century, the overall FVC in Xinjiang primarily exhibited a trend of growth, exhibiting a rate of increase of 4 × 10−4 y−1. The multi-year average FVC was 0.223. The mean value of the multi-year FVC was 0.223, and the mean values of different ecological zones showed the following order: R1 > R3 > R2 > R5 > R4. (2) The predominant spatial pattern of FVC across Xinjiang’s landscape is characterized by higher coverage in the northwest and lower in the southeast. In this region, 66.63% of the terrain exhibits deteriorating vegetation, while 11% of the region exhibits a notable rise in plant growth. Future changes in FVC will be dominated by a decreasing trend. Regarding the coefficient of variation outcomes, a minor variation, representing 42.12% of the total, is noticeable; the mean coefficient of variation stands at 0.2786. The stability across varied ecological zones follows the order: R1 > R3 > R2 > R4 > R5. (3) Factors that have a facilitating effect on vegetation FVC included relative humidity, daylight hours, and precipitation, with relative humidity having a greater influence, while factors that have a hindering effect on vegetation FVC included air temperature and wind speed, with wind speed having a greater influence. (4) Vegetation alterations are primarily influenced by climate change, while human activities play a secondary role, contributing 56.93% and 43.07%, respectively. This research underscores the necessity for continued surveillance of vegetation dynamics and the enhancement of policies focused on habitat renewal and the safeguarding of vegetation in Xinjiang.

Geo-ecological, shoreline dynamic, and flooding impacts of Cyclonic Storm Mocha: A geospatial analysis

This research comprehensively assesses the aftermath of Cyclonic Storm Mocha, focusing on the coastal zones of Rakhine State and the Chittagong Division, spanning Myanmar and Bangladesh. The investigation emphasizes the impacts on coastal ecology, shoreline dynamics, flooding patterns, and meteorological variations. Employed were multiple vegetation indices—Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Modified Vegetation Condition Index (mVCI), Disaster Vegetation Damage Index (DVDI), and Fractional Vegetation Cover (FVC)—to evaluate ecological consequences. The Digital Shoreline Assessment System (DSAS) aided in determining shoreline alterations pre- and post-cyclone. Soil exposure and flood extents were scrutinized using the Bare Soil Index (BSI) and Modified Normalized Difference Water Index (MNDWI), respectively. Additionally, the study encompassed an analysis of microclimatic variables, comparing meteorological data across pre- and post-cyclone periods. Findings indicate significant ecological impacts: an estimated 8985.46 km2 of dense vegetation (NDVI >0.6) was adversely affected. Post-cyclone, there was a discernible reduction in EVI values. The mean mVCI shifted negatively from −0.18 to −0.33, and the mean FVC decreased from 0.39 to 0.33. The DVDI underscored considerable vegetation damage in various areas, underscoring the cyclone's extensive impact. Meteorological analysis revealed a 245 % increase in rainfall (20.22 mm on May 14, 2023 compared to the May average of 5.86 mm), and significant increases in relative humidity (14 %) and wind speed (205 %). Erosion was observed along 74.60 % of the studied shoreline. These insights are pivotal for developing comprehensive strategies aimed at the rehabilitation and conservation of critical coastal ecosystems. They provide vital data for emergency response initiatives and offer resources for entities engaged in enhancing coastal resilience and protecting local community livelihoods.

Variability of urban fractional vegetation cover and its driving factors in 328 cities in China

Variability of urban fractional vegetation cover and its driving factors in 328 cities in China

Assessing the effects of climate and human activity on vegetation change in Northern China

Fractional vegetation cover (FVC) has changed significantly under various disturbances over northern China in recent decades. This research examines the dynamics of FVC and how it is affected by climate and human activity during the period of 1990–2018 in northern China. The effects of climate change (i.e., temperature, precipitation, solar radiation, and soil moisture) and human activity (socioeconomic data and land use) on vegetation coverage change in northern China from 1990 to 2018 were quantified using the Sen + Mann-Kendall test, partial correlation analysis, and structural equation modelling (SEM) methods. The findings of this research indicate the following: (1) From 1990 to 2018, the overall trend in FVC in northern China was increased. The areas with obvious increases were mainly situated on the northern slope of Tianshan Mountains, Xinjiang, the Loess Plateau, the Northeast China Plain, and the Sanjiang Plain, while the areas with distinct degradation were located in the Inner Mongolia Plateau, the Changbai Mountain and the eastern part of north China. (2) In the past 29 years, the FVC in northern China has been mainly affected by precipitation and soil moisture. (3) Based on structural equation modelling, we discovered that certain variables impacted the main factors influencing the amount of FVC in northern China. Human activity has had a larger impact on FVC than climate change. Our findings can accelerate the comprehension of vegetation dynamics and their underlying mechanisms and provide a theoretical basis for regional ecological environmental protection.

Analysis of Factors Influencing Spatial Distribution of Soil Erosion under Diverse Subwatershed Based on Geospatial Perspective: A Case Study at Citarum Watershed, West Java, Indonesia.

The application of remote sensing data has been significant in modeling soil erosion. However, previous studies have fallen short in elucidating and lacked an understanding of the multifactor influencing erosion. This study addresses these limitations by employing the InVEST and the Geodetector models. Specifically, it aims (1) to delineate both spatial and temporal variations in soil erosion within the Citarum watershed from 2010 to 2020, (2) to identify the key drivers of soil erosion and unravel the underlying mechanisms, and (3) to identify the high-risk zones for soil erosion. Both models consider a range of natural predictors, including topography (slope factor), climate (precipitation factor), and vegetation cover (vegetation factor). In addition, they incorporate social parameters such as income per capita and population density, which interact with the watershed's position in the downstream, middle, and upper streams. The results reveal that, over a decade, the average soil erosion increased by 15.50 × 106 tons, marking a 16.65% surge. The impact of factors varies significantly across different subwatershed areas. For example, fraction vegetation cover interactions influence upper- and middle-stream regions, while the downstream area is notably affected by precipitation interactions. The high-risk erosion areas in the watershed are primarily influenced by slope, precipitation, and fractional vegetation cover. In these areas, factors causing high erosion risks include slope, precipitation, and other environmental variables categorized into strata. The study highlights the varying influential factors in different watershed areas.

A study of spatial distribution and dynamic change in monthly FVC of urban parks.

The study on the spatial distribution and dynamic change in monthly Fractional Vegetation Cover (FVC) of parks provides a scientific basis for vegetation management and optimization in urban parks. This research focuses on two comprehensive parks located in Xinxiang, China-People's Park and Harmony Park, using multi-spectral Unmanned Aerial Vehicle (UAV) images as the data source and considering monthly periods. Monthly FVC data was obtained using the method of Dimidiate Pixel Model based on the Normalized Difference Vegetation Index (NDVI). The dynamic changes of monthly FVC at regional scale were described through the dynamic changes in the monthly FVC mean and in the FVC areas at various scales, and the dynamic changes in the monthly FVC were analyzed using the coefficient of variation and curve change trends. Furthermore, the dynamic changes in FVC areas at various scales in the parks were analyzed using standard deviation and curve change trends. Subsequently, the differential method was used to analyze the monthly FVC dynamic changes at pixel scale. The results indicate: (1) In terms of the spatial distribution characteristics in monthly FVC of urban parks, both parks exhibit the highest ratio of bare area in January and February. The proportions of FVC for People's Park are 59.17% and 64.46%, while for Harmony Park they are 69.10% and 51.92%, showing the most distinct spatial distribution characteristics. The high and very high coverage areas in each month are mainly distributed on the outskirts of the park, while the medium, medium-low, and low coverage areas are mainly located in the central and middle parts of the park. The overall FVC of the park shows a trend of high coverage on the periphery and low coverage in the center. (2) In the spatial-temporal dynamic change in FVC at regional scale, the average monthly FVC changes exhibit an overall "∩" -shaped pattern. The peak and minimum FVC values for different parks occur at different times. The peak FVC for People's Park appears in August, while for Harmony Park it appears in June, with corresponding FVC values of 0.46 and 0.50, respectively. The minimum FVC for People's Park occurs in February, and for Harmony Park it occurs in January, with FVC values of 0.17 and 0.15, respectively. Among the dynamic change in FVC areas at various scales, the areas of bare and highest-coverage exhibit the greatest fluctuations, with the ascending and descending changes and rates of bare and highest-coverage areas generally showing opposite trends. (3) In terms of the spatial-temporal dynamic changes in FVC at pixel scale in urban parks, overall, FVC shows moderate improvement from February-August, and moderate degradation from January-February and from August-December. The degradation and improvement are primarily slight. The most significant improvement in monthly FVC occurs in March-April, with a predominant type of significant improvement in FVC changes. People's Park and Harmony Park show the most significant degradation in FVC during September-October and October-November, respectively, with a predominant type of significant degradation in FVC changes. During the periods of most significant improvement and degradation in monthly FVC, the spatial distribution of significant improvement and degradation areas primarily occurs in the periphery and middle parts of the parks. FVC in urban parks decreases from January to February and from August to December, while it increases from February to August, with relatively good conditions from June to August. Vegetation optimization should consider: balancing recreational and ecological functions overall, controlling the proportion of bare land, and enhancing the canopy structure of vegetation in low coverage areas or the coverage of hard surfaces; locally increasing the proportion of evergreen plants and moderately increasing planting density. In addition, parks should strengthen management to reduce the impact of flooding and maintain the health of vegetation.

Spatiotemporal characteristics of vegetation cover change in the Central Yunnan urban agglomeration from 2000 to 2020 based on Landsat data and its driving factors

The study of dynamic changes in vegetation coverage has significant implications for urban clusters in terms of ecological environment protection, climate change research, water resource management and urban planning. A thorough understanding of the driving mechanisms behind vegetation coverage helps provide scientific basis and policy recommendations for the ecological environment protection, sustainable development and decision-making of urban clusters. Taking the central Yunnan urban cluster as an example, this study utilizes long-term Landsat remote sensing image data from 2000 to 2020 on the Google Earth Engine platform. The fractional vegetation cover (FVC) is estimated using a pixel-based binary model. The Sen’s slope, Mann–Kendall trend analysis and Hurst index methods are employed to investigate the spatiotemporal dynamic characteristics of FVC. The impacts of climate change and human activities on vegetation coverage are explored through partial correlation analysis and residual analysis. Finally, considering natural factors such as topography and climate, as well as socioeconomic factors, the geographic detector is used to quantitatively analyze the driving factors behind FVC changes. The results indicate that: (1) From 2000 to 2020, the FVC in the central Yunnan urban cluster changed significantly, showing an overall improvement trend. The average FVC is 0.496 with a growth rate of 0.0024 per year, and it exhibits a distribution pattern of higher values in the west and lower values in the east. (2) In terms of percentage distribution, areas with low FVC and high FVC account for a relatively high proportion. The trend of FVC changes is as follows: improvement (49.6%) > degradation (26.4%) > no change (24%). The average Hurst value is 0.45, indicating that future FVC changes in the study area will be opposite to the past. (3) FVC shows a positive correlation with precipitation and a negative correlation with temperature. Human activities have a positive impact on FVC in the study area, accounting for 55.1% of the regional proportion. (4) Slope and nighttime light contribute the most to FVC. The explanatory power of the interaction between slope and precipitation/temperature is the most significant.

Quantitative analysis of fractional vegetation cover in southern Sichuan urban agglomeration using optimal parameter geographic detector model, China

Vegetation is a main part of ecosystems and an essential indicator for monitoring changes in terrestrial ecosystems. It is crucial for us to discover the temporal and spatial features and potential drivers of vegetation change to promote regional ecological environment protection and management. However, it can be difficult to pinpoint the causes of vegetation change when considering both human activity and climate change. We used trend and stability method to study the temporal and spatial patterns of vegetation evolution in the South Sichuan Urban Agglomeration (SSUA) from 2001 to 2021 with the Google Earth Engine (GEE) platform. An optimal parameter-based geographical detector (OPGD) model was applied to optimize the spatial scale and zoning effect of geographic data, effectively solving the problem of spatial data heterogeneity. It compensates for the inadequacies of conventional approaches that neglect the modifiable areal units problem (MAUP), and improving the science and accuracy of quantitative analysis and identification of vegetation drivers. We studied demonstrate that (1) During the last 21 years, Fractional Vegetation Cover (FVC) has generally been in good condition, with a multi-year average FVC greater than 0.4 of 71.74 %, and the vegetation is significantly characterized by a low fluctuation of 78.16 %. However, there is a significant trend of vegetation degradation, accounting for 8.89 %, mainly in the main urban areas of Neijiang and Zizhong County, Lu County of Luzhou City, Gao County of Yibin City, and other areas with rapid urbanization. In general, FVC is low in the built-up areas of towns and along transportation roads, while the mountainous and agricultural areas have a high level of vegetation cover. (2) The OPGD model detection showed the optimal spatial scale of vegetation cover in this study region was 2 km. Optimal discrete parameter combinations for slope, elevation, temperature and GDP are quantile breaks with 9 intervals, which contribute to improved scientific accuracy and precision in studies of vegetation change and its drivers. (3) The explanatory power of urbanization rate, land use type, slope, GDP, population density, and average annual precipitation were all above 20 % and were the main drivers of vegetation change. Moreover, any two factors interacted in a nonlinear enhancement and a bi-variable enhancement, increasing the impact on vegetation spatial variation. When the slope is 26.9°∼87.4°, the elevation is 967 m ∼ 4207 m, the average annual temperature is 0.18 °C ∼ 13.6 °C, the average annual precipitation is 328 mm ∼ 439 mm, the GDP is 4.07 ∼ 5.23 million yuan km−2, the population density is 12.7 ∼ 21.1 people/km2, the urbanization rate is 33.4 %∼37.7 %, and the land-use type is forest land, the FVC value is the highest and suitable for vegetation growth. The study showed that using the OPGD model to detect the zoning effects and spatial scale of the explanatory variables solves for the shortcomings of the previous methods for variable regional units and discrete methods, may more precisely explore the features of temporal and spatial changes in the vegetation and the driving mechanisms, offers scientific references for environmental conservation and long-term economic growth in the region.

Corrigendum to: Utilisation of unmanned aerial vehicle imagery to assess growth parameters in mungbean (Vigna radiata (L.) Wilczek)

Context Unmanned aerial vehicles (UAV) with red–green–blue (RGB) cameras are increasingly used as a monitoring tool in farming systems. This is the first field study in mungbean (Vigna radiata (L.) Wilzcek) using UAV and image analysis across multiple seasons. Aims This study aims to validate the use of UAV imagery to assess growth parameters (biomass, leaf area, fractional light interception and radiation use efficiency) in mungbean across multiple seasons. Methods Field experiments were conducted in summer 2018/19 and spring–summer 2019/20 for three sowing dates. Growth parameters were collected fortnightly to match UAV flights throughout crop development. Fractional vegetation cover (FVC) and computed vegetation indices: colour index of vegetation extraction (CIVE), green leaf index (GLI), excess green index (ExG), normalised green-red difference index (NGRDI) and visible atmospherically resistant index (VARI) were generated from UAV orthomosaic images. Key results (1) Mungbean biomass can be accurately estimated at the pre-flowering stage using RGB imagery acquired with UAVs; (2) a more accurate relationship between the UAV-based RGB imagery and ground data was observed during pre-flowering compared to post-flowering stages in mungbean; (3) FVC strongly correlated with biomass (R2 = 0.79) during the pre-flowering stage; NGRDI (R2 = 0.86) showed a better ability to directly predict biomass across the three experiments in the pre-flowering stages. Conclusion UAV-based RGB imagery is a promising technology to replace manual light interception measurements and predict biomass, particularly at earlier growth stages of mungbean. Implication These findings can assist researchers in evaluating agronomic strategies and considering the necessary management practices for different seasonal conditions.

Study of spatio-temporal variation in fractional vegetation cover and its influencing factors in Xinjiang, China

Study of spatio-temporal variation in fractional vegetation cover and its influencing factors in Xinjiang, China

UAV-Assisted Dynamic Monitoring of Wheat Uniformity toward Yield and Biomass Estimation.

Crop uniformity is a comprehensive indicator used to describe crop growth and is important for assessing crop yield and biomass potential. However, there is still a lack of continuous monitoring of uniformity throughout the growing season to explain their effects on yield and biomass. Therefore, this paper proposed a wheat uniformity quantification method based on unmanned aerial vehicle imaging technology to monitor and analyze the dynamic changes in wheat uniformity. The leaf area index (LAI), soil plant analysis development (SPAD), and fractional vegetation cover were estimated from hyperspectral images, while plant height was estimated by a point cloud model from RGB images. Based on these 4 agronomic parameters, a total of 20 uniformity indices covering multiple growing stages were calculated. The changing trends in the uniformity indices were consistent with the results of visual interpretation. The uniformity indices strongly correlated with yield and biomass were selected to construct multiple linear regression models for estimating yield and biomass. The results showed that Pielou's index of LAI had the strongest correlation with yield and biomass, with correlation coefficients of -0.760 and -0.801, respectively. The accuracies of the yield (coefficient of determination [R 2] = 0.616, root mean square error [RMSE] = 1.189 Mg/ha) and biomass estimation model (R 2 = 0.798, RMSE = 1.952 Mg/ha) using uniformity indices were better than those of the models using the mean values of the 4 agronomic parameters. Therefore, the proposed uniformity monitoring method can be used to effectively evaluate the temporal and spatial variations in wheat uniformity and can provide new insights into the prediction of yield and biomass.

Characterization and drivers of water and carbon fluxes dynamics in dune ecosystems of the Horqin Sandy Land

Sandy regions constitute pivotal components of terrestrial ecosystems, exerting significant influences on global ecological equilibrium and security. This study meticulously explored water and carbon fluxes dynamics within a dune ecosystem in the Horqin Sandy Land throughout the growing seasons from 2013 to 2022 by employing an advanced eddy covariance system. The dynamic characteristics of these fluxes and their underlying driving forces were extensively analyzed, with a particular focus on the impact of precipitation. The main results are as follows: (1) During the growing seasons of 2015 and 2016, the dune ecosystem acted as a modest carbon source, while in 2013, 2014, and 2017– 2022, it transformed into a net carbon sink. Notably, the annual mean values of water use efficiency (WUE) and evapotranspiration (ET) were 5.16 gC·kg−1H2O and 255.4 mm, respectively. (2) The intensity, frequency, and temporal distribution of precipitation were found to significantly influence the carbon and water fluxes dynamics. Isolated minor precipitation events did not trigger substantial fluctuations, but substantial and prolonged precipitation events spanning multiple days or consecutive minor precipitation events resulted in notable assimilation delays. (3) Air temperature, soil temperature, and fractional vegetation cover (FVC) were found to be key factors influencing the carbon and water fluxes. Specifically, FVC exhibited a negative logarithmic correlation with net ecosystem CO2 exchange (NEE) and a power function relationship with WUE. (4) The interaction between carbon and water fluxes is exhibited by exponential increases in ecosystem respiration (Reco) and gross primary productivity (GPP) with WUE, while NEE displayed an exponential decrease in relation to WUE. These findings are of high significance in predicting the potential ramifications of climate change on the intricate carbon and water cycles, and enhance our understanding of ecosystem dynamics in sandy environments.

Critical influencing factors on vegetation productivity in sandy land of the Northwestern Liaoning Province,China

To reveal the key factors influencing vegetation productivity in sandy lands, we conducted a comprehensive analysis of vegetation productivity on regional scale, pixel scale, and plot scale of the sandy lands in northwes-tern Liaoning Province, based on soil physicochemical data, topographical data, climate data, and the intrinsic characteristics of vegetation. On the regional scale, we established a random forest model to explore the impact of topographical factors, climate factors, and vegetation characteristics on vegetation productivity. On the pixel scale, we performed a correlation analysis between vegetation cover and climate factors. On the plot scale, we combined the physicochemical properties of 234 soil samples with topographical factors and vegetation characteristics, and utilized the random forest model to calculate the importance values of each factor. The results showed that soil nutrients could explain 24.8% of the spatial variation in net primary productivity when other factors were excluded. When introducing topographical factors into the model, the model could explain 40% variation of net primary productivity. When further incorporating fractional vegetation coverage and leaf area index into the model, the model could explain 72.8% variation of net primary productivity. Our findings suggested that fractional vegetation coverage and leaf area index were the most influential factors affecting vegetation productivity in this area. Topographical factors ranked second, followed by climate factors, which had a relatively small impact.

Cascade effects of climate and vegetation influencing the spatial variation of evapotranspiration in China

Evapotranspiration (ET) is generally affected by climate and vegetation, but the main factor controlling the spatial variation of ET largely varied with different regions. Clarifying the characteristics and dominant factor of ET in China is essential for understanding the regional ecosystem water balance. This study quantitatively assessed the magnitude and spatial variations of ET in China's terrestrial ecosystems using data measured by eddy covariance from 150 observation sites and investigated its underlying direct and indirect influencing factors. The results demonstrated that the ET of China's terrestrial ecosystems varied from 146.95 to 1125.33 mm, with an average value of 551.00 ± 204.12 mm. The ET showed a general increasing trend from the North to South, aligning with the latitudinal patterns of water availability (mean annual precipitation (MAP)), energy conditions (mean annual net radiation (MARn), and mean annual temperature (MAT)). Compared to regions at the same latitude, the North China Plain exhibited relatively higher ET because of the extensive cropland sites in the area. Furthermore, significant positive linear correlations were found between ET and MAT in the majority of ecosystems. However, a negative correlation of ET with MAT was observed in grasslands, which may be attributed to inconsistent responses of ET to MAT on the Tibetan Plateau and Inner Mongolia. Fractional vegetation coverage (FVC) exhibited a more significant direct influence on ET than climate factors (MARn, MAT, and MAP). Nevertheless, climate factors not only exert a direct influence on ET but also have cascade effects that affect FVC, thereby indirectly influencing the ET. When considering these cascade effects, MAP emerged as the dominant driver of spatial variations in ET of China's terrestrial ecosystems. This study provides essential information on variations of ET in China's terrestrial ecosystem and valuable insights for future explorations into the adaptive mechanisms underlying spatial variations of ET.

Evaluation of urban ecological benefits utilizing vegetated areas metrics in Nanjing, China

Vegetation areas are vital for enhancing the sustainability of urban ecosystems. However, ongoing urbanization gradually reduces these areas in cities, leading to a decline in ecological benefits. This study integrates fractional vegetation cover and land surface temperature in understanding the changes in vegetated areas and ecological benefits from 1990 to 2020 in eight central districts of Nanjing using the CITYgreen model and geographically weighted regression. The findings show that human intervention resulted in a reduction of urban vegetated areas by 22.84%, while non-vegetated areas have increased by 1.24 times. The total value of ecological benefits was reduced by approximately 57.41%. The landscape pattern experienced gradual fragmentation, evidenced by a decrease of approximately 62% in the largest patch index (LPI). Both the mean shape index (SHAPE-MN) and landscape shape index (LSI) showed significant increases, indicating a rise in the complexity of landscape patches due to human intervention.

FVC-Net： A fusion network for producing fine spatial resolution fractional vegetation cover

FVC-Net： A fusion network for producing fine spatial resolution fractional vegetation cover