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Based on the MODIS NDVI and meteorological data from 2000 to 2018, the CA-Markov model and correlation analysis method were used to quantitatively analyze the future trend and climate response of fractional vegetation coverage in Hotan Area. The main conclusions were as follows. (1) In recent 19 years, the area of non-vegetation coverage, medium, and high vegetation coverage increased, while the area of low vegetation coverage decreased. The proportion of slight degradation and slight improvement of FVC was 9.04% and 9.47%, respectively. The proportion of areas with obvious degradation and improvement was 1.22% and 0.83%, respectively. (2) Based on the prediction of fractional vegetation coverage in 2024, it was found that low vegetation coverage and high vegetation coverage would increase significantly, while the areas of non-vegetation coverage and medium vegetation coverage would decrease. (3) The overall vegetation coverage in Hotan Area was low from 2000 to 2018. The high vegetation coverage areas were mainly located in plain oases and alluvial plains in front of the Kunlun Mountains with better hydrothermal conditions, and the middle and low vegetation coverage areas were mainly distributed in the oasis-desert transition zone. The non-vegetated areas were mainly concentrated in the Taklimakan Desert, where there was little precipitation and strong evaporation, and the Kunlun Mountains, where the temperature was extremely low. In areas without human activities, the dynamic changes in vegetation coverage were mainly affected by climatic factors, while in plain oasis areas with frequent human activities, vegetation coverage was affected by the dual effects of human activities and climate. [ABSTRACT FROM AUTHOR]

Long time series of vegetation monitoring can be carried out by remote sensing data, the level of urban greening is objectively described, and the spatial characteristics of plant pollen are indirectly understood. Pollen is the main allergen in patients with seasonal allergic rhinitis. Meteorological factors affect the release and diffusion of pollen. Therefore, studying of the complex relationship between meteorological factors and allergic rhinitis is essential for effective prevention and treatment of the disease. In this study, we leverage remote sensing data for a comprehensive decade-long analysis of urban greening in Tianjin, which exhibits an annual increase in vegetative cover of 0.51 per annum, focusing on its impact on allergic rhinitis through changes in pollen distribution. Utilizing high-resolution imagery, we quantify changes in urban Fractional Vegetation Coverage (FVC) and its correlation with pollen types and allergic rhinitis cases. Our analysis reveals a significant correlation between FVC trends and pollen concentrations, with a surprising value of 0.71, highlighting the influence of urban greenery on allergenic pollen levels. We establish a robust connection between the seasonal patterns of pollen outbreaks and allergic rhinitis consultations, with a noticeable increase in consultations during high pollen seasons. our findings indicate a higher allergenic potential of herbaceous compared to woody vegetation. This nuanced understanding underscores the importance of pollen sensitivity, alongside concentration, in driving allergic rhinitis incidents. Utilizing a Generalized Linear Model, significant features influencing the number of visits for allergic rhinitis (P < 0.05) were identified. Both GLM and LSTM models were employed to forecast the visitation volumes for rhinitis during the spring and summer-autumn of 2022. Upon validation, it was found that the R² values between the simulated and actual values for both GLM and LSTM models surpassed the 95% confidence threshold. Moreover, the R² values for the summer-autumn seasons (GLM: 0.56, LSTM: 0.72) were higher than those for spring (GLM: 0.22, LSTM: 0.47). Comparing the errors between the simulated and actual values of GLM and LSTM models, LSTM exhibited higher simulation precision in both spring and summer-autumn seasons, demonstrating superior simulation performance. Overall, our study pioneers the integration of remote sensing with meteorological and health data for allergic rhinitis forecasting. This integrative approach provides valuable insights for public health planning, particularly in urban settings, and lays the groundwork for advanced, location-specific allergenic pollen forecasting and mitigation strategies. Highlights: The incidence of allergic rhinitis is primarily determined by pollen allergenicity, followed by concentration. Utilizing high-resolution imagery, we quantify changes in urban Fractional Vegetation Coverage (FVC) and its correlation with pollen types and allergic rhinitis cases. Weaving together remote sensing, meteorological, and health data, we employed Generalized Linear Models and LSTM models to dissect the determinants of allergic rhinitis and to construct a daily predictive model for the incidence of allergic rhinitis consultations. [ABSTRACT FROM AUTHOR]

The Middle Route of the South-to-North Water Diversion Project is a critical infrastructure that ensures optimal water resource distribution across river basins and safeguards the livelihood of people in China. This study investigated its effects on the land surface temperature (LST) and fractional vegetation coverage (FVC) in the Danjiang River Basin. Moreover, it examined the spatial and temporal patterns of this project, providing a scientific basis for the safe supply of water and ecological preservation. We used the improved interpolation of mean anomaly (IMA) method based on the digital elevation model (DEM) to reconstruct LST while FVC was estimated using the image element dichotomous model. Our findings indicated a general increase in the average LST in the Danjiang River Basin post-project implementation. During both wet and dry seasons, the cooling effect was primarily observed in the south-central region during the daytime, with extreme values of 6.1 °C and 5.9 °C. Conversely, during the nighttime, the cooling effect was more prevalent in the northern region, with extreme values of 3.0 °C and 2.3 °C. In contrast, the warming effect during both seasons was predominantly located in the northern region during the daytime, with extreme values of 5.3 °C and 5.5 °C. At night, the warming effect was chiefly observed in the south-central region, with extreme values of 5.8 °C and 5.9 °C. FVC displayed a seasonal trend, with higher values in the wet season and overall improvement over time. Statistical analysis revealed a negative correlation between vegetation change and daytime temperature variations in both periods (r = −0.184, r = −0.195). Furthermore, a significant positive correlation existed between vegetation change and nighttime temperature changes (r = 0.315, r = 0.328). Overall, the project contributed to regulating LST, fostering FVC development, and enhancing ecological stability in the Danjiang River Basin. [ABSTRACT FROM AUTHOR]

The Shule River Basin (SRB) is a typical arid area in northwest China with a fragile ecology. Understanding vegetation dynamics and its response to climate change and human activities provides essential ecological and environmental resource management information. This study extracted fractional vegetation coverage (FVC) data from 2000 to 2019 using the Google Earth Engine platform and Landsat satellite images, employing trend analysis and other methods to examine spatiotemporal changes in vegetation in the SRB. Additionally, we used partial correlation and residual analyses to explore the response of FVC to climate change and human activities. The main results were: (1) The regional average FVC in the SRB showed a significant upward trend from 2000 to 2019, increasing by 1.3 × 10−3 a–1. The area within 1 km of roads experienced a higher increase of 3 × 10−3 a–1, while the roadless areas experienced a lower increase of 1.1 × 10−3 a–1. The FVC spatial heterogeneity in the SRB is significant. (2) Partial correlation analysis shows that the FVC correlates positively with precipitation and surface water area, with correlation coefficients of 0.575 and 0.744, respectively. A weak negative correlation exists between the FVC and land surface temperature (LST). FVC changes are more influenced by precipitation than by LST. (3) The contributions of climate change to vegetation recovery are increasing. Human activities, particularly agricultural practices, infrastructure development, and the conversion of farmland to grassland, significantly influence vegetation changes in densely populated areas. (4) The area changes of different land types are closely related to climate factors and human activities. Increased construction, agricultural activity, and converting farmland back to grassland have led to an increase in the area proportions of "impervious surfaces", "cropland", and "grassland". Climate changes, such as increased rainfall, have resulted in larger areas of "wetlands" and "sparse vegetation". These results provide valuable information for ecosystem restoration and environmental protection in the SRB. [ABSTRACT FROM AUTHOR]

The situation of rocky desertification in the southwestern part of China is very serious and has been included as one of three major ecological problems. In this study, using Landsat images as the data sources, we estimated the fractional vegetation coverage (FVC) in the Mengdong River Basin in the Hunan Province over the past 40 years, analyzed its spatio-temporal variation characteristics, and explored the driving mechanism (climate and anthropogenic) using the Mann–Kendall, Hurst index, and partial correlation methods. Specifically, the impact of ecological engineering on the recovery of vegetation cover in rocky desertification areas was analyzed and discussed in this study. The results demonstrate the following: (1) The changes in FVC in the study area before and after the rocky desertification management ecological project differed significantly, with a very small change in the rate of change in the mean value of FVC between 1987 and 1999 (<0.1%), while the FVC had a significant linear growth trend between 2000 and 2022 (>0.9%). (2) The Hurst index of FVC ranged from −0.233 to 2.476, with an average value of 0.864. The area with an H value greater than 0.75 accounted for 80.12%, indicating that the future trend in the vast majority of regions will develop in accordance with the current change trend. (3) The average partial correlation coefficients between FVC and precipitation and between FVC and temperature were −0.02 and 0.27, respectively, showing that FVC is more sensitive to temperature than precipitation. The combination of climate change and human activities is the main cause of FVC change. The contributions of climate change (precipitation and temperature) and human activities to FVC variation are about 30% and 70%, respectively. Ecological restoration projects have a significant positive effect on the recovery of vegetation in rocky desertification areas. The results of this study are intended to provide a scientific basis for analyzing the characteristics of vegetation restoration in existing rocky desertification areas and ecological management in future rocky desertification areas. [ABSTRACT FROM AUTHOR]

Study region: Loess Plateau (LP), China Study focus: Evapotranspiration (ET) is crucial in the terrestrial hydrological cycle and serves as a vital indicator of water resource availability. However, the interactions between ET and vegetation, climate, and soil on the LP have been left unexplored, especially during the growing season and regarding ET thresholds for different land-use types. Therefore, we used ridge regression and geographic detector model to analyze ET response to fractional vegetation coverage (FVC), temperature, precipitation, land surface temperature (LST), and temperature vegetation dryness index (TVDI). Additionally, the elastic coefficient was used to determine the ET thresholds for dominant factors in different land-use types. New hydrological insight for the region: The study reveals FVC as the dominant factor driving ET changes, while also highlighting the synergistic effects of temperature, precipitation, LST, and TVDI with FVC. The synchronization of ET and FVC change trends in vegetation unchanged and restored areas underscores the significance of vegetation cover in regulating regional water cycles. Furthermore, the study provides valuable insights into the regional water cycle dynamics and offers a certain level of guidance for water management strategies tailored to different land-use types.

An in-depth exploration of fractional vegetation coverage (FVC) changes and driving mechanisms in the Yangtze River Delta (YRD) is crucial for maintaining regional ecological health and achieving sustainable development. We therefore calculate FVC in the YRD from 2012 to 2021 based on MODIS NDVI data, and analyze its spatiotemporal evolution. Multiple regression residual analysis and geographic detector model were used along with various auxiliary data to further explore its driving mechanisms in a hierarchical manner. Finally, the Hurst index was used to forecast future trends in FVC. The results show that: 1) Significant changes in FVC occurred around 2016, rapidly declining at a rate of 8.9×10-3a-1 from 2012 to 2016, followed by fluctuating growth at a rate of 5.2×10-3a-1 thereafter. 2) Although the overall development of FVC in the YRD tends toward stability, the fluctuation was pronounced in the Taihu Lake Basin. Improvement areas were mainly concentrated on both sides of the Yangtze River in the central part of the YRD and in the southern mountainous region. Degraded areas were concentrated in cities north of the Huai River. 3) Dynamic changes in FVC were primarily driven by a combination of climate and human activities, whereas spatial heterogeneity was mainly driven by factors such as elevation, slope, and landform type. The improvement in the explanatory power of anthropogenic factors for spatial heterogeneity when combined with natural factors was significant.

[Objective] The dynamic changes of vegetation and its response relationship with topography in Changbai Mountain were explored in order to provide scientific support for ecological environmental protection and management in this mountainous area. [Methods] Based on MODIS NDVI and DEM data, fractional vegetation cover in Changbai Mountain from 2000 to 2020 was estimated using a pixel dichotomy model. Spatiotemporal variations of fractional vegetation cover were analyzed in depth using Sen+Mann-Kendall trend analysis, spatial autocorrelation analysis, and a center of gravity migration model. At the same time, the differential effects of vegetation cover variations were quantitatively determined by combining the analysis results with the correction coefficient for topographic areas in this study in terms of elevation, slope, and aspect factors. [Results] ① In terms of spatiotemporal distribution, the fractional vegetation cover in Changbai Mountain increased at a rate of 0.023 7/10 yr (p

Fractional vegetation coverage (FVC) is an important ecological parameter reflecting the growth of regional plants. Existing FVC estimation is often based on vegetation indices, especially the Normalized Difference Vegetation Index (NDVI). However, NDVI can be oversaturated and easily affected by problems such as ‘shadow’ in images, which leads to a decrease in the precision of FVC estimation. In this study, the Normalized Shaded Vegetation Index (NSVI) was used to comprehensively compare the estimation ability of FVC with NDVI, and the differences in FVC estimation ability between NSVI and NDVI were explored. Based on two dimensions of ‘bright’ and ‘shadow’ hierarchies and FVC ranks, four evaluation systems of signal-to-noise ratio (SNR), index range, statistical model and dimidiate pixel model were selected from Sentinel-2A MSI, Landsat-8 OLI and Resource-1-02D (ZY1-02D) images and covered a variety of topographic landscapes. The results showed that: (1) the ability of NSVI to resist soil background is slightly smaller than NDVI; (2) the range of NSVI and NDVI in hyperspectral images is slightly larger than multispectral images, and the ability of NSVI to detect vegetation information in medium-high-rank and high-rank FVC areas has obvious advantages; (3) in the same kind of regression model, the goodness of fit of NSVI, NDVI and FVC was slightly higher in bright areas than in shaded areas, and the goodness of fit of the five regression models obtained from NSVI showed a significant advantage in bright areas compared with NDVI, with the best fit of the cubic curve model; (4) the estimation accuracy of the dimidiate pixel model based on NSVI and NDVI is slightly higher in bright areas than in shaded areas, and the estimation ability of NSVI is slightly better than that of NDVI in bright areas with medium-high-rank and high-rank FVC. NSVI and NDVI have their own advantages in SNR, index range, statistical model and dimidiate pixel model, so it is suggested that when remote sensing estimation of FVC is carried out, NSVI should be preferred in medium-high-rank and high-rank FVC areas, and NDVI should be preferred in low-rank FVC areas in shaded areas.

Rapid climate variability and intense human activities generate obvious impacts on the Qilian Mountains ecosystem. The time series of fractional vegetation coverage (FVC) from 1986 to 2021 were used to quantify the impact of climate variability and human activities on vegetation variations in the Qilian Mountain National Nature Reserve (QMNNR), using 3147 land satellite images based on the Google Earth Engine cloud platform. The contributions of climate variability and human activities to FVC were quantified using multiple regression residual analysis. Partial correlation and correlation methods were used to quantify the impact of temperature, precipitation, and human activity footprints on FVC. The results showed that from 1986 to 2021, the increase rate of FVC was 1.7 × 10−3 y−1, and the high vegetation coverage of the FVC was mainly distributed in the southeastern part of the reserve. In contrast, the low vegetation coverage was mainly distributed in the northwest part of the reserve. The Mann–Kendall mutation test found that the year of 2009 was the year of the mutation. The growth rate of FVC from 2010 to 2021 was greater than that from 1986 to 2009. In addition, climate variability and human activities exhibited a remarkable spatial heterogeneity in FVC changes. Climate variability and human activities contributed 49% and 51% to the increase in FVC in the reserve, respectively, and the contribution of human activities was greater than that of climate variability. The warming and humidification phenomena in the reserve were obvious. However, precipitation was the dominant factor affecting the dynamic changes in FVC. This study improves our understanding of the response of vegetation dynamics to the climate and human activities in the QMNNR. [ABSTRACT FROM AUTHOR]

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The Xiong'an New Area was officially established in 2018 to construct a new, intelligent, and efficient urban area to alleviate Beijing's non-capital functions. Using Landsat satellite images, we employed the dimidiate pixel model, band operation, and transition matrix to analyze the temporal and spatial variations in FVC (Fractional Vegetation Coverage) within the Xiong'an New Area in 2005, 2013, and 2019, respectively. Urbanization rate, precipitation, temperature, and population were considered potential driving forces, which we analyzed using grey relational analysis and linear regression to explore the correlation between FVC and these factors. The findings are as follows: from 2005 to 2019, overall improvement and significant degradation have been observed. In Baiyangdian, a part of the national key ecological area, water bodies and FVC have increased. Grey relational analysis revealed that precipitation had the highest grey relational value of 0.76. The average correlation among natural factors was 0.67, while that among human factors was 0.60. Generally, the Xiong'an New Area vegetation exhibited instability, while Baiyangdian demonstrated relatively stable FVC. Grey relational analysis indicates a strong potential for social and economic development in the Xiong'an New Area. [ABSTRACT FROM AUTHOR]

Copyright of Bulletin of Soil & Water Conservation is the property of Bulletin of Soil & Water Conservation Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Understory vegetation plays an important ecological role in maintaining the diversity of the ecosystem, the stability of ecosystem services, and the accumulation of nutrient elements, as an important part of a forest ecosystem. In this study, a new method of recognizing areas without understory vegetation is proposed. The method makes full use of the advantages of spectral characteristics, spatial structure information and temporal resolution of UAV images, and can quickly and simply distinguish understory, without vegetation cover. Combined with fractional vegetation coverage (FVC) and vegetation dispersion, understory, with no vegetation area, can be successfully recognized, and the Pr, Re and F1 are all above 85%. The proportion of bare soil under forest in our study area is 20.40%, 19.98% and even 41.69%. The study area is located in Changting County, Fujian Province, which is a typical red soil area in China where serious soil erosion is taking place in the forest. The method provides a promising, quick and economic way of estimating understory vegetation coverage with high spatial accuracy. [ABSTRACT FROM AUTHOR]

[Objective] The vegetation change and important climate factors influencing fractional vegetation coverage (FVC) in Sichuan Province were analyzed in order to provide theoretical support for the sustainable development of natural resources in this area. [Methods] Based on the MODIS-NDVI dataset for Sichuan Province from 2000 to 2020, FVC for the study area from 2000 to 2020 was statistically analyzed, and its spatiotemporal variation characteristics and relationships with climate factors were analyzed. [Results] ① The FVC of Sichuan Province tended to be stable, and the average FVC value was about 0.50. ② FVC exhibited obvious spatial heterogeneity. FVC in the east was higher than in the west, and presented an increasing spatial distribution pattern from northwest to southeast. ③ The areas with high FVC in Sichuan Province accounted for about 70% of the total area, and the overall vegetation status was good. There were signs of slow growth in FVC over time. ④ FVC changes in the study area were positively and negatively correlated with air temperature and precipitation, and the area proportions were close to each other. ⑤ The driving forces of FVC change in Sichuan Province were mainly non-climate factors, and the area driven by climate factors accounted for 21.17% of the total area. [Conclusion] Sichuan Province has complex topography. The good climatic conditions, vigorous vegetation growth, and stable FVC experienced over a period of many years has maintained the ecological environment of the middle and upper reaches of the Yangtze River. In the future, Planners and officials should give more attention to the impacts of human activities FVC, and actively build an ecological barrier in the middle and upper reaches of the Yangtze River.

[Objective] The temporal and spatial variation of vegetation coverage in the Kubuqi Desert during 1989—2019 was explored in order to provide the necessary theoretical basis and support for ecological restoration of the local area. [Methods] Landsat images acquired every five years from 1989 to 2019 was used, combined with the normalized difference vegetation index (NDVI) model, ENVI 5.3, and ArcGIS 10.2 to analyze the fractional vegetation coverage (FVC) variation of the Kubuqi Desert during 1989—2019. [Results] ① In terms of temporal variation, the average fractional vegetation coverage of the Kubuqi Desert increased from 0.104 to 0.243 during 1989—2019. In terms of spatial distribution, fractional vegetation coverage increased from west to east and north in the Kubuqi Desert. ② The fractional vegetation coverage of the Kubuqi Desert declined during 1999—2004 and 2009—2014. The average fractional vegetation coverage for those two periods decreased by 0.053 and 0.054, respectively, and the degraded areas were 3 870.22 km2 and 6 093.59 km2, respectively. [Conclusion] On the whole, the vegetation coverage of the Kubuqi Desert increased during 1989—2019. In the future, local ecological restoration efforts should focus on the low vegetation coverage areas.

As one of the most important components of terrestrial ecosystems, vegetation is essential for maintaining ecosystem functions. Excavating the independent effect and coupling of climate and topography on vegetation variation is conducive to the high-quality development of the regional ecological environment. Based on the remote sensing data of Landsat 5/7/8 and Sentinel-2, the spatiotemporal evolution of Fractional vegetation coverage (FVC) from 1990 to 2020 is explored with the Google Earth Engine (GEE). The individual effect and coupling of climate and topography factors are discussed through the geographic detector model, taking the Yellow River Basin of Henan Province (YRBHP) as the study area. The results show that: (1) The eastern and western parts of the YRBHP are densely covered with vegetation, and the central area is scarce. The FVC of the YRBHP has improved significantly from 1990 to 2020, and the proportion of improved areas is 67.7%. (2) Elevation is the strongest driving factor of FVC in the YRBHP, and the contribution rate to FVC was 0.3. (3) The coupling effect of climate and topography factors enhances the effect on FVC, among which the strongest coupling effect is elevation and precipitation. The results could dig deeper by analyzing the coupling relationship among the driving factors for providing the suitable environment condition for vegetation restoration.

Fractional vegetation coverage (FVC) is an important ecological parameter reflecting the growth of regional plants. Existing FVC estimation is often based on vegetation indices, especially the Normalized Difference Vegetation Index (NDVI). However, NDVI can be oversaturated and easily affected by problems such as 'shadow' in images, which leads to a decrease in the precision of FVC estimation. In this study, the Normalized Shaded Vegetation Index (NSVI) was used to comprehensively compare the estimation ability of FVC with NDVI, and the differences in FVC estimation ability between NSVI and NDVI were explored. Based on two dimensions of 'bright' and 'shadow' hierarchies and FVC ranks, four evaluation systems of signal-to-noise ratio (SNR), index range, statistical model and dimidiate pixel model were selected from Sentinel-2A MSI, Landsat-8 OLI and Resource-1-02D (ZY1-02D) images and covered a variety of topographic landscapes. The results showed that: (1) the ability of NSVI to resist soil background is slightly smaller than NDVI; (2) the range of NSVI and NDVI in hyperspectral images is slightly larger than multispectral images, and the ability of NSVI to detect vegetation information in medium-high-rank and high-rank FVC areas has obvious advantages; (3) in the same kind of regression model, the goodness of fit of NSVI, NDVI and FVC was slightly higher in bright areas than in shaded areas, and the goodness of fit of the five regression models obtained from NSVI showed a significant advantage in bright areas compared with NDVI, with the best fit of the cubic curve model; (4) the estimation accuracy of the dimidiate pixel model based on NSVI and NDVI is slightly higher in bright areas than in shaded areas, and the estimation ability of NSVI is slightly better than that of NDVI in bright areas with medium-high-rank and high-rank FVC. NSVI and NDVI have their own advantages in SNR, index range, statistical model and dimidiate pixel model, so it is suggested that when remote sensing estimation of FVC is carried out, NSVI should be preferred in medium-high-rank and high-rank FVC areas, and NDVI should be preferred in low-rank FVC areas in shaded areas. Comparing FVC estimation ability in bright and shaded areas of multi-source images Effectiveness of NSVI and NDVI for estimating FVC are better in bright areas NSVI is preferred in areas with MH-rank and H-rank FVC NDVI is preferred in areas with L-rank FVC in shaded areas NSVI has broader application and improvement prospects in estimating FVC [ABSTRACT FROM AUTHOR]

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Copyright of Coal Science & Technology (0253-2336) is the property of Coal Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The construction of the Three Gorges Reservoir and the resettlement project have caused increasing contradictions between human and land, and led to the deterioration of the ecological environment. In order to ameliorate ecological environment of the Three Gorges Area, the government carried out several ecological restoration projects to improve the vegetation coverage from 1990s. This paper aims to quantitatively analyze the impact of ecological projects on the vegetation in the Three Gorges Area of Chongqing, China. Landsat and MODIS data from 1992 to 2015 were used to estimate vegetation coverage. In addition, the land cover data of the European Space Agency (ESA) was used to explore the impact of ecological projects on land cover change. The cropland accounted for about 62% and the forestland accounted for about 34% of the total area. There was more than 90% of the study area covered with high or very high vegetation coverage. From 1992 to 2015, a total of 272.7 km2 croplands were converted into forestland in the Ecological Migration Project (EMP), 795.6 km2 in the Grain for Green Project (GGP), and 13.77 km2 in the Ecological Restoration Zone Project (ERZP). Among the three projects, the GGP was the most powerful measure, with a contribution rate of 1.6%. The implementation of the ecological projects improved vegetation coverage, which indicated that the ecological projects measures were effective in ecological restoration. [ABSTRACT FROM AUTHOR]

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Copyright of Arid Zone Research / Ganhanqu Yanjiu is the property of Arid Zone Research Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Applied Ecology / Yingyong Shengtai Xuebao is the property of Chinese Journal of Applied Ecology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Vegetation has become the main producer to serve as an integral part of natural ecosystem, as well as perform various vital ecological functions. Vegetation coverage has some direct effects on the structure, functions, and health of an ecosystem. The vegetation coverage can also be one of the key ecological parameters that can reflect the level and quality of an ecosystem, particularly on Xinjiang, China, characterized by shortage of water resources and fragile ecological environment in the arid hinterland of Eurasia. New challenge has posed on the ecological system in Xinjiang, as "the Belt and Road" initiative is being promoted. Furthermore, the area and quality of vegetation coverage is degrading, as adverse effects increased from the climate change and human activities in the last 20 years, leading to severe negative impacts on the ecological environment and social economy. Therefore, it is necessary to carry out the long-term sequence monitoring on the vegetation coverage at the regional scale, in order to promote the eco-friendly development model, and further to ensure the sustainable development of the "Silk Road of Green Development". In this study, MODIS/EVI data was employed to obtain the Fractional Vegetation Coverage (FVC) based on pixel dichotomy, thereby to explore the change in vegetation coverage and its driving mechanisms. The variation of FVC was revealed using the percentage of deviation from normal and linear regression analysis. The driving mechanism of FVC change was also analyzed by the gravity center model and correlation analysis. The results showed that: 1) The average vegetation coverage was 0.255 in Xinjiang from 2000 to 2019, where the area with an very low coverage accounting for 63.53%. The vegetation coverage was higher in Yili River Valley, where the area with an extremely high coverage accounting for 77.9%, whereas that in southern Xinjiang only accounted for 6.94%. 2) The FVC was on the rise in Xinjiang as a whole, with a stable variation. The percentage of abnormal change was within the normal range. However, the fluctuation of FVC was relatively large in eastern Xinjiang. In 2015, the FVC value was nearly 20% higher than the average level, with other regions fluctuating in the range of ±5%. The reduction area of vegetation coverage was larger than that of the increasing area, where 53.04% of the area tended to decrease. At the level of significance less than 0.05, the area with increasing trend was large. 3) In the driving mechanisms for the variation of vegetation coverage, the spatial position of vegetation centers, temperature and precipitation were calculated to analyze the change and relationship between the gravity centers of various elements from the spatial level. The vegetation center of gravity fluctuated to high longitude and high latitude. In terms of longitude change, temperature was the main driving factor of vegetation spatial change in Xinjiang, indicating a positive correlation between the center of vegetation and temperature. There was a negative correlation between the vegetation center and precipitation center in Yili River Valley, with R value of -0.492. However, there was no significant relationship between the vegetation center and the center of temperature and precipitation in eastern Xinjiang. The significant change of vegetation coverage was less than 0.05 under the level of low vegetation coverage and uneven distribution. The increased area was greater than the reduced area, indicating an obvious effect of ecological restoration. A recommendation can be to strengthen the vegetation protection, especially in the area of reduced vegetation coverage. The evaluation of vegetation change and its driving mechanisms in different regions of Xinjiang can provide a scientific basis to protect the fragile ecological environment. [ABSTRACT FROM AUTHOR]

Fractional Vegetation Coverage (FVC) is an essential indicator that captures variations in vegetation and documents the impacts of climate change and human activity for environmental assessment. However, conventional methods encounter challenges in accurately extracting fine-scale FVC in drylands due to the vegetation distribution being very heterogeneous in space with patches and inter-patches. Using the lower Tarim River Basin as a typical study case, we investigated three deep convolutional neural networks—Unet, Pspnet, and Deeplabv3 + —to generate high-precision FVC in drylands with high-resolution (0.8 m) remote sensing images. Among these models, the Unet model performed better, with an accuracy of 93.38%, while the accuracy of Pspnet and Deeplabv3+ was 88.14% and 88.91%, respectively. Comparison with the FVC derived from normalized difference vegetation index (NDVI), and land use/land cover data from ESRI and ESA indicated that the FVC map produced by Unet was more consistent with on-site field observations. Delving into drivers influencing dryland FVC, we found that groundwater depth plays a pivotal role compared to topographical and climatic variables. Specifically, when the groundwater depth exceeds −3 m, the probability of occurring high FVC is reduced to 50%. This study innovatively extracted the FVC of drylands with high vegetation spatial heterogeneity, which better solves the insufficient accuracy of the existing dataset, serves as a valuable reference for monitoring vegetation change, and facilitates more precise quantification of carbon storage. • Deep convolutional neural network is effective in extracting FVC in drylands. • Unet performs best in extract both sparse and dense vegetation cover in drylands. • Groundwater has a wider impact on dryland FVC than climatic and topographic factors. [ABSTRACT FROM AUTHOR]

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The vegetation coverage of temperate grasslands in China has changed substantially due to climate change during the past decades, which significantly affects the function of grassland ecosystems. To appropriately carry out adaptive management and protect temperate grassland vegetation, it is important to understand the variations in fractional vegetation coverage (FVC) of China's temperate grasslands and how they are responding to climate change. Using the GIMMS NDVI and climatic datasets, this study explored the dynamics of FVC and their climatic drivers across the temperate grassland region of China during 1982∼2015. The results showed that the growing season mean FVC increased by 0.12% per year during 1982∼2015. The increases in precipitation and minimum temperature in the growing-season (especially in spring) could enhance the FVC of various vegetation types. In summer, the FVC of temperate steppe and desert steppe could drastically increase with increasing precipitation. In addition, this study found that the impacts of daytime and night-time warming on the FVC of temperate grasslands were asymmetric. Daytime warming can moderately increase FVC of temperate grasslands, while night-time warming could significantly increase it. Furthermore, the increase in summer daytime and night-time temperatures leads to a weak decrease and a moderate increase in FVC, respectively. This asymmetric effect was more evident for the temperate steppe and desert steppe in the central area. In autumn, the temperatures increase had significant positive impacts on the FVC of temperate meadows and steppes. This study highlights the differences in the impacts of climate change at different time scales on the FVC of grasslands with various vegetation types, and indicates that the asymmetric influences of daytime and night-time temperatures in different seasons on FVC must be included in calculating the vegetation coverage of China's temperate grasslands. The results could provide information for maintaining grassland ecosystem functions and managing environmental systems. • The FVC of temperate grasslands has increased by 0.12% per year from 1982 to 2015. • Increases in growing season precipitation and minimum temperature enhanced the FVC. • Nighttime and daytime temperatures have asymmetric effects on the FVC. [ABSTRACT FROM AUTHOR]

Until 2015, China had established 2740 nature reserves with a total area of 1.47 million km2, covering 14.8% of China’s terrestrial land surface. Based on remote sensing inversion, ecological model simulation and spatial analysis methods, we analyzed the spatial and temporal variations of fractional vegetation coverage (FVC), net primary production (NPP), and human disturbance (HD) in habitats of typical national nature reserves (NNRs) during the first 15 years of the 21st century from 2000 to 2015. And then the three indicators were compared between different NNR types and varied climate zones. The results showed that (1) the average 5-year FVC of NNRs increased from 36.3% to 37.1%, and it improved in all types of NNRs to some extent. The annual average FVC increased by 0.11%, 0.84%, 0.21%, 0.09%, 0.11% and 0.08% in NNRs of forest ecosystem, plain meadow, inland wetland, desert ecosystem, wild animal and wild plant, respectively. (2) The NPP annually increased by 2.06 g·m-2, 1.23 g·m-2, 0.28 g·m-2 and 0.4 g·m-2 in NNRs of plain meadow, inland wetland, desert ecosystem and wild animal, respectively. However, it decreased by 3.45 g·m-2 and 2.35 g·m-2 in NNRs of forest ecosystem and wild plant respectively. (3) In the past 15 years, besides the slight decreases in the NNRs located at the Qinghai-Tibet Plateau and the south subtropical zone, HD enhanced in most of NNRs, especially HD in the warm temperate humid zone increased from 4.7% to 5.35%. [ABSTRACT FROM AUTHOR]

Rainwater-induced soil erosion occurring in the forest is a special phenomenon of soil erosion in many red soil areas. Detection of such soil erosion is essential for developing land management to reduce soil loss in areas including southern China and other red soil regions of the world. Remotely sensed canopy cover is often used to determine the potential of soil erosion over a large spatial scale, which, however, becomes less useful in forest areas. This study proposes a new remote sensing method to detect soil erosion under forest canopy and presents a case study in a forest area in southern China. Five factors that are closely related to soil erosion in forest were used as discriminators to develop the model. These factors include fractional vegetation coverage, nitrogen reflectance index, yellow leaf index, bare soil index and slope. They quantitatively represent vegetation density, vegetation health status, soil exposure intensity and terrain steepness that are considered relevant to forest soil erosion. These five factors can all be derived from remote sensing imagery based on related thematic indices or algorithms. The five factors were integrated to create the soil erosion under forest model (SEUFM) through Principal Components Analysis (PCA) or a multiplication method. The case study in the forest area in Changting County of southern China with a Landsat 8 image shows that the first principal component-based SEUFM achieves an overall accuracy close to 90%, while the multiplication-based model reaches 81%. The detected locations of soil erosion in forest provide the target areas to be managed from further soil loss. The proposed method provides a tool to understand more about soil erosion in forested areas where soil erosion is usually not considered an issue. Therefore, the method is useful for soil conservation in forest.

Soil moisture, especially surface soil moisture (SSM), plays an important role in the development of various natural hazards that result from extreme weather events such as drought, flooding, and landslides. There have been many remote sensing methods for soil moisture retrieval based on microwave or optical thermal infrared (TIR) measurements. TIR remote sensing has been popular for SSM retrieval due to its fine spatial and temporal resolutions. However, because of limitations in the penetration of optical TIR radiation and cloud cover, TIR methods can only be used under clear sky conditions. Microwave SSM retrieval is based on solid physical principles, and has advantages in cases of cloud cover, but it has low spatial resolution. For applications at the local scale, SSM data at high spatial and temporal resolutions are important, especially for agricultural management and decision support systems. Current remote sensing measurements usually have either a high spatial resolution or a high temporal resolution, but not both. This study aims to retrieve SSM at both high spatial and temporal resolutions through the fusion of Moderate Resolution Imaging Spectroradiometer (MODIS) and Land Remote Sensing Satellite (Landsat) data. Based on the universal triangle trapezoid, this study investigated the relationship between land surface temperature (LST) and the normalized difference vegetation index (NDVI) under different soil moisture conditions to construct an improved nonlinear model for SSM retrieval with LST and NDVI. A case study was conducted in Iowa, in the United States (USA) (Lat: 42.2°~42.7°, Lon: -93.6°~-93.2°), from 1 May 2016 to 31 August 2016. Daily SSM in an agricultural area during the crop-growing season was downscaled to 120-m spatial resolution by fusing Landsat 8 with MODIS, with an R² of 0.5766, and RMSE from 0.0302 to 0.1124 m³/m³. [ABSTRACT FROM AUTHOR]

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The paper provides a contribution for populating database of three physical parameters needed to model energy performance of buildings with green roofs: “coverage ratio” ( σ f ), leaf area index (LAI) and leaf temperature ( T f ). On purpose, six plant species were investigated experimentally: Phyla nordiflora, Aptenia lancifolia , Mesembryanthenum barbatus , Gazania nivea, Gazania uniflora , and Sedum . Proper ranges of the cited parameters have been found for each species. The here indicated ranges of σ f values refer to different growth levels of the species in the same lapse of time, that is four months. Single measured LAI values are also reported for the same plants. As for the T f (upper and lower layer), ranges of revealed temperatures refer to those detected from 10:30 a.m. to 16:30 p.m. of a selected day. Additionally, the dependence of T f on climatic parameters was investigated. A linear equation resulted the best fitting curve for all experimental T f data and the corresponding solar radiation data (with autocorrelation coefficients between 0.80 and 0.98). Furthermore, the effect potentially produced on building energy consumption by these species was analyzed using a simulation tool. Estimated cooling energy savings range approximately between 8% and 20% depending on adopted plants. [ABSTRACT FROM AUTHOR]

Several studies analyze the thermal performance of vegetated roofs, presenting either mathematical models or experimental quantifications of heat transfer process through them, also showing the effect of vegetation and soil parameters on the thermal and energy performance of this type of roofing system. However, presently the level of availability of these parameters, has not been enough considered. This work intends to investigate this underestimated issue of the green roofs’ thermal modeling, through the consideration of the availability of parameters pertinent to the shortwave radiation exchange, which are adopted by models based on leaf area index (LAI) and on the fractional vegetation coverage (σ f ). From the analysis of these models, the importance of a precise knowledge of such parameters arises, despite the availability of their values is still limited. In addition, the absence of a proper database including several green roof plant species and information on their stage of growth, to which a technician possibly will refer in the case of lack of field data, represents quite a worthwhile matter because it may lead to inaccurate estimations of the thermal loads for climatization of buildings equipped with green roofs. In this respect, the manuscript reports an estimation of the errors potentially occurring when not specific vegetation data are used. This evaluation is based on simulations, performed with the DesignBuilder © software, showing the energy effects produced by different assumptions in the green parameters (that is by adopting experimental data revealed by authors and a potential mix of the literature available parameters). Thus, the mission of the paper is to enlighten a difficulty that a technician possibly will encounter when performing an energy and thermal simulation of a green roof; therefore a strong call for making the current database more tailored on green roofs has also arisen. [ABSTRACT FROM AUTHOR]

Long-term remote sensing normalized difference vegetation index (NDVI) datasets have been widely used in monitoring vegetation changes. In this study, the NASA Global Inventory Modeling and Mapping Studies (GIMMS) NDVI3g dataset was used as the data source, and the dimidiate pixel model, intensity analysis, and residual analysis were used to analyze the changes of vegetation coverage in Inner Mongolia--from 1982 to 2010--and their relationships with climate and human activities. This study also explored vegetation changes in Inner Mongolia with respect to natural factors and human activities. The results showed that the estimated vegetation coverage exhibited a high correlation (0.836) with the actual measured values. The increased vegetation coverage area (49.2% of the total area) was larger than the decreased area (43.3%) from the 1980s to the 1990s, whereas the decreased area (57.1%) was larger than the increased area (35.6%) from the 1990s to the early 21st century. This finding indicates that vegetation growth in the 1990s was better than that in the other two decades. Intensity analysis revealed that changes in the average annual rate from the 1990s to the early 21st century were relatively faster than those in the 1980s-1990s. During the 1980s-1990s, the gain of high vegetation coverage areas was active, and the loss was dormant; in contrast, the gain and loss of low vegetation coverage areas were both dormant. In the 1990s to the early 21st century, the gains of high and low vegetation coverage areas were both dormant, whereas the losses were active. During the study period, areas of low vegetation coverage were converted into ones with higher coverage, and areas of high vegetation coverage were converted into ones with lower coverage. The vegetation coverage exhibited a good correlation (R² = 0.60) with precipitation, and the positively correlated area was larger than the negatively correlated area. Human activities not only promote the vegetation coverage, but also have a destructive effect on vegetation, and the promotion effect during 1982 to 2000 was larger than from 2001 to 2010, while, the destructive effect was larger from 2000 to 2010. [ABSTRACT FROM AUTHOR]

Remotely sensed land surface temperature and fractional vegetation coverage (LST/FVC) space has been widely used in modeling and partitioning land surface evaporative fraction (EF) which is important in managing water resources. However, most of such models are based on conventional trapezoid and simply determine the wet edge as air temperature (Ta) or the lowest LST value in an image. We develop a new Two-source Model for estimating EF (TMEF) based on a two-stage trapezoid coupling with an extension of the Priestly-Taylor formula. Latent heat flux on the wet edge is calculated with the Priestly-Taylor formula, whereas that on the dry edge is set to 0. The wet and dry edges are then determined by solving radiation budget and energy balance equations. The model was evaluated by comparing with other two models that based on conventional trapezoid (i.e., the Two-source Trapezoid Model for Evapotranspiration (TTME) and a One-source Trapezoid model for EF (OTEF)) in how well they simulate and partition EF using MODIS products and field observations from HiWATER-MUSOEXE in 2012. Results show that the TMEF outperforms the other two models, where EF mean absolute relative deviations are 9.57% (TMEF), 15.03% (TTME), and 30.49% (OTEF). [ABSTRACT FROM AUTHOR]

A viable solution to reducing the buildings’ energy demand is the implementation of green coverings that have been demonstrated to improve the envelope performance, especially in summer season. Anyway, to apply computer models analyzing the energy performance of buildings equipped with such components, the knowledge of specific parameters is needed. Among these, the fractional vegetation coverage and leaf area index are of great importance. By means of an experimental facility, first results of the evaluation of these foliage-related parameters for five types of vegetation are presented. [ABSTRACT FROM AUTHOR]

Climate change often leads to the vulnerability of vegetation cover, while the impact of human activities on vegetation cover is undoubtedly more complex in this context, especially in Ethiopia. This paper analyzed the spatiotemporal dynamics of vegetation growth in Ethiopia from 2003 to 2018 by the enhanced vegetation index (EVI) based on different time scales and explored the coefficient of variation and driving factors of the fractional vegetation coverage (FVC). The results indicated that the EVI mainly presents a "double peak" pattern, with large spatiotemporal differences between quarters and months in Ethiopia. The FVC increased by 0.0005 per year, but vegetation showed a browning trend after 2013. The FVC degraded area accounted for 43.9% of the total area, of which the significantly degraded area accounted for 7.51% due to human activities, mainly in northern, central, and southern Ethiopia. The effects of precipitation and maximum temperature on vegetation differed on time scales. Spatially, the vegetation on the northwest side of the Main Ethiopian Rift Valley (MERV) was dominated by a combination of maximum temperature and precipitation, while vegetation on the southeast side of MERV was mainly influenced by precipitation. However, the spatial overlay analysis with degraded and healthy vegetation zones revealed that human activities were the key driver of vegetation cover change rather than climate change. This study provides support for further development of vegetation health conservation policies in Ethiopia and monitoring of vegetation dynamics in other countries around the world. • The annual EVI values show a "double peak" pattern in Ethiopia. • The dominant climatic factors on the southeast and northwest sides of MERV differ. • Ethiopia's FVC showed an overall growth trend with an annual growth rate of 0.05%. • Human activities dominate the dynamic evolution of vegetation in Ethiopia. [ABSTRACT FROM AUTHOR]

Abstract: Grassland monitoring is important for both global change research and regional sustainable development. Gross primary production (GPP) is one of the key factors for understanding grass growing conditions. Methods for estimating GPP are plentiful, and the light use efficiency (LUE) model based on remote sensing data is widely used. The MODIS GPP product, which is employed by the National Aeronautics and Space Administration (NASA), is calculated using the LUE model and the surface reflection data from the Moderate Resolution Imaging Spectroradiometer onboard the Terra/Aqua satellite. The MODIS GPP product harbors its own uncertainties arising from the sources and parameters, such as FPAR and light use efficiency (ɛ). In this study, we propose an improved indicator for monitoring grassland based on MODIS GPP and NDVI data. Fractional vegetation coverage and the percentage of grass area (1km2) were used to reduce the mixed pixel effect. A function of NDVI was used to simulate the light use efficiency and FPAR. The modified GPP data were calculated and validated with in situ measured data from the Sichuan province, China, 2011. The results indicated that the modified GPP data were a more accurate indicator for monitoring grassland than previous indicators, and the precision of grass production simulated by SsGPPndvi reached 85.6%. Spatial statistic results were consistent with the practical condition in most cases. Since MODIS data are available twice a day, the improved indicator can meet the actual requirement of grassland monitoring at regional scale. [Copyright &y& Elsevier]

It is important to determine long-term changes in vegetation cover, and the associated driving forces, to better understand the natural and human-induced factors affecting vegetation growth. We calculated the fractional vegetation coverage (FVC) of the Urumqi River basin and selected seven natural factors (the clay and sand contents of surface soils, elevation, aspect, slope, precipitation and temperature) and one human factor (land use type). We then used the Sen-Man-Kendall method to calculate the changing trend of the FVC from 2000 to 2020. We used the optimal parameters-based geographical detector (OPGD) model to quantitatively analyze the influence of each factor on the change in vegetation coverage in the basin. The FVC of the Urumqi River basin fluctuated from 2000 to 2020, with average values between 0.22 and 0.33. The areas with no and low vegetation coverage accounted for two-thirds of the total area, whereas the areas with a medium, medium-high and high FVC accounted for one-third of the total area. The upper reaches of the river basin are glacial and forest areas with no vegetation coverage and a high FVC. The middle reaches are concentrated in areas of urban construction with a medium FVC. The lower reaches are in unstable farmland with a medium and high FVC and deserts with a low FVC and no vegetation. From the perspective of the change trend, the areas with an improved FVC accounted for 62.54% of the basin, stable areas accounted for 5.66% and degraded areas accounted for 31.8%. The FVC showed an increasing trend in the study area. The improvement was mainly in the areas of urban construction and desert. Degradation occurred in the high-elevation areas, whereas the transitional zone was unchanged. The analysis of driving forces showed that the human factor explained more of the changes in the FVC than the natural factors in the order: land use type (0.244) > temperature (0.216) > elevation (0.205) > soil clay content (0.172) > precipitation (0.163) > soil sand content (0.138) > slope (0.059) > aspect (0.014). Apart from aspect, the explanatory power ( Q value) of the interaction of each factor was higher than that of the single factor. Risk detection showed that each factor had an interval in which the change in the FVC was inhibited or promoted. The optimum elevation interval of the study area was 1300-2700 m and the greatest inhibition of the FVC was seen above 3540 m. Too much or too little precipitation inhibited vegetation coverage.

Based on the sub-pixel analysis model, and by using 2000-2009 MODIS NDVI (250 m resolution), this paper quantitatively analyzed the spatiotemporal change characteristics and their causes of fractional vegetation coverage (FVC) in Shannxi Province. From 2000 to 2009, the FVC in the Province had a significant increasing trend, with the great magnitude of 35.0%. During that period, the vegetation coverage increased from 56.9% in 2000 to 68.9% in 2009 in the provincial scale, and the increment was much higher in northern Shannxi, being 21.6% in Yulin and 22.0% in Yan'an. Though the vegetation coverage had an overall increase, it was locally degraded in some areas. The areas with improved vegetation coverage at the significance levels of <0.01, and <0.05 were 37.8% and 11.9%, while those with non-improved and degraded vegetation coverage were 46.1% and 4.2%, respectively. The areas whose vegetation coverage had a change rate of 200%, 200% -100%, 100% -10%, 10% -10%, and < -10% occupied 12.2%, 13.3%, 38.8%, 29.3%, and 6.4% of the total, respectively. During the study period, the structure of vegetation coverage in the Province also improved. The areas with high and normal vegetation density increased significantly by 10% and 8.4%, respectively, while the area with low vegetation density decreased significantly by 18.4%. The improvement of the FVC in Shaanxi Province was the interactive effect of natural factors and human activities, but the main cause was the implementation of a series of ecological construction projects such as closing hill for forestation and restoring farmland into forestland and grassland. [ABSTRACT FROM AUTHOR]

• Driving factors of the terminal lake evolution are evaluated by regression analysis. • Ecological water conveyance (EWC) dominates the evolution of Taitema Lake. • Before EWC, land use was the dominant factor affecting the FVC of the lake. Inland river terminal lakes help maintain the regional ecological environment. However, terminal lakes in arid regions have shrunk dramatically over the past 50 years. We extracted the water areas of Taitema Lake in the arid region of Central Asia and calculated the fractional vegetation coverage (FVC) of the surrounding areas based on Landsat images from 1989 to 2019. We analysed the evolution and driving factors of the FVC and lake area before and after implementing an ecological water conveyance (EWC) project in 2000. The contributions of the driving factors were derived using regression analysis, and the effect coefficients were calculated with a path analysis. Results showed that before EWC (1989–1999), Taitema Lake was dry, the proportions of medium and high FVC areas were small, and FVC varied moderately. The contributions of land use and inflow water from Qarqan River to the regional FVC were 57.4% and 45.3%, respectively, and the effect coefficients of temperature and precipitation on FVC were 0.09 and 0.02, respectively. Human-dominated land-use change was the main factor affecting FVC change. After EWC (2000–2019), the water in Taitema Lake was restored, and the average annual proportions of medium and high FVC were 9.5% and 2.7% higher than the pre-EWC period, respectively. The contributions of land use and river inflow to the water area were 39.1% and 60.9%, and those to FVC were 40.7% and 59.3%, respectively. Precipitation had a slightly stronger effect on the water area and FVC than the temperature in the study area, whereas the influence of river inflow on the water area and FVC was less evident than that of the EWC. EWC can replenish lake water quickly, which is essential for improving the ecological environment of terminal lake areas. [ABSTRACT FROM AUTHOR]

• Fractional vegetation coverage and surface roughness schemes were modified in CoLM. • The overestimation of LST is reduced by the revised CoLM. • The revised model performs better in simulating LST in summer than in winter. • Contributions of each component of the energy budget to LST changes were identified. The application of Land surface models (LSMs) at regional scales still faces challenges and large uncertainties exist in simulating regional energy fluxes and land surface temperature (LST). This is possibly caused by their poor representations of surface parameter properties (e.g., fractional vegetation coverage (FVC) and surface roughness length (z 0m)) to effectively capture vegetation dynamics. In this study, satellite-based FVC and z 0m schemes were incorporated into the Common Land Model (CoLM) and their impacts on modeling energy fluxes and associated LST were further assessed over grassland and forest areas in China. Our results show that the annual mean LST was obviously overestimated by the original CoLM for both grassland and forest. The overestimation was reduced by the revised CoLM when compared with the Moderate Resolution Imaging Spectroradiometer (MODIS) LST products. Concerning the seasonal patterns, the revised model performed evidently better in simulating LST in summer than in winter. Additionally, the decreased simulation of LST was dominated by a reduction of the surface absorbed short-wave radiation when applying the revised FVC scheme over both grassland and forest, while the increased sensible/latent heat fluxes contributed most to the reduction of LST for the revised z 0m scheme in grassland/forest, respectively, indicating that land surface energy partitioning is sensitive to the FVC and z 0m parameterizations. Results from this study suggest that the use of satellite-based dynamics of FVC and z 0m parameterizations in the CoLM provides a valuable improvement in regional applications. [ABSTRACT FROM AUTHOR]

The demonstration area of ecologically friendly development in the Yangtze River Delta is a major national strategy area, and thus eco-environmental protection should be a priority for the sustainable development. Exploring the spatial-temporal variations of franctional vegetation coverage (FVC) is conducive to accurate assessing the ecological quality of environment, which is of great significance to regional sustainable development. In this study, the characteristics and trends of spatial-temporal variation of vegetation cover during 1984-2019 in the demonstration area were analyzed based on Google Earth Engine (GEE). The effects of different ecological factors on FVC were quantified by the boosted regression tree (BRT). Results showed that,the changing trend of FVC in the study area shifted from decreasing to increasing trend from 1984 to 2019. The spatial distribution of FVC in the study area varied with both stages and regions. FVC was mainly degraded. Compared with the year 1984, the area of vegetation degradation and improvement in 2019 accounted for 49.8% and 12.8%, respectively. The vegetation degradation mainly occurred in the north of Wujiang, south of Jiashan, and northeast of Qingpu. Human activities severely weakened the influence of natural factors on FVC. Our findings suggested that the GEE is an effective tool for monitoring the dynamics of vegetation coverage.

Based on MODIS-EVI data and binary pixel model, we calculated fractional vegetation coverage (FVC) in Pisha sandstone area of Northwest China from 2000 to 2018. The topographic effects on fractional vegetation coverage was analyzed from elevation, slope, and aspect using digital elevation model (DEM) data. From 2000 to 2018, FVC in Pisha sandstone area of Northwest was relatively low, which increased with a rate of 2.43·a -1 . The positive anomaly of FVC existed in 12 years, with higher positive anomaly FVC mainly in 2018 and 2013. The negative anomaly of FVC existed in seven years, with the lower negative anomaly of FVC mainly in 2000 and 2001. The spatial distribution of FVC in Pisha sandstone area decreased from southeast to northwest, which differed significantly among different types, with that in bare Pisha sandstone being the lowest, followed by sand-covered Pisha sandstone, and soil-covered Pisha sandstone area being the highest. The FVC in this area had an average increasing rate of 0.0031, suggesting an improved vegetation growth status. Analysis of elevation-aspect effect showed that aspect had a great influence on FVC at the elevation no more than 1000 m and more than 1500 m in bare Pisha sandstone and sand-covered Pisha sandstone areas, respectively. The aspect of soil-covered Pisha sandstone had a small influence on FVC, its influence was relatively large at 1200-1300 m. When the slope of Pisha sandstone area was no more than 15°, the slope-aspect effect of FVC was not obvious. When the slope was more than 25°, FVC of shady and semi-shady slope in bare and sand-covered Pisha sandstone areas was larger than that of sunny and semi-sunny slopes. For soil-covered Pisha sandstone, when the slope was more than 15°, FVC of semi-shady and semi-sunny slopes was larger than that of shady and sunny slopes.