Assessment of vegetation cover changes and the contributing factors in the Al-Ahsa Oasis using Normalized Difference Vegetation Index(NDVI)

The abandonment of date palm grove of the former Al-Ahsa Oasis in the eastern region of Saudi Arabia has resulted in the conversion of delicate agricultural area into urban area.The current state of the oasis is influenced by both expansion and degradation factors.Therefore,it is important to study the spatiotemporal variation of vegetation cover for the sustainable management of oasis resources.This study used Landsat satellite images in 1987,2002,and 2021 to monitor the spatiotemporal variation of vegetation cover in the Al-Ahsa Oasis,applied multi-temporal Normalized Difference Vegetation Index(NDVI)data spanning from 1987 to 2021 to assess environmental and spatiotemporal variations that have occurred in the Al-Ahsa Oasis,and investigated the factors influencing these variation.This study reveals that there is a significant improvement in the ecological environment of the oasis during 1987–2021,with increase of NDVI values being higher than 0.10.In 2021,the highest NDVI value is generally above 0.70,while the lowest value remains largely unchanged.However,there is a remarkable increase in NDVI values between 0.20 and 0.30.The area of low NDVI values(0.00–0.20)has remained almost stable,but the region with high NDVI values(above 0.70)expands during 1987–2021.Furthermore,this study finds that in 1987–2002,the increase of vegetation cover is most notable in the northern region of the study area,whereas from 2002 to 2021,the increase of vegetation cover is mainly concentrated in the northern and southern regions of the study area.From 1987 to 2021,NDVI values exhibit the most pronounced variation,with a significant increase in the“green”zone(characterized by NDVI values exceeding 0.40),indicating a substantial enhancement in the ecological environment of the oasis.The NDVI classification is validated through 50 ground validation points in the study area,demonstrating a mean accuracy of 92.00%in the detection of vegetation cover.In general,both the user’s and producer’s accuracies of NDVI classification are extremely high in 1987,2002,and 2021.Finally,this study suggests that environmental authorities should strengthen their overall forestry project arrangements to combat sand encroachment and enhance the ecological environment of the Al-Ahsa Oasis.

Spatial analysis of vegetation cover response to climate trends in Khakassia(South Siberia)

An analysis of the changes in vegetation cover on the territory of the Republic of Khakassia in 2000–2021 due to climatic trends was carried out based on the MODIS data.The changes in vegetation cover were estimated based on trends in Normalized Difference Vegetation Index(NDVI)and Enhanced Vegetation Index(EVI).In general,in the 21st century,an increase in the biomass of vegetation cover is observed.Positive trends were observed in 16%–22%of the territory,and negative only in 1%–3%.For about 20%of the analyzed territory,a significant influence of climate on the changes in vegetation cover was revealed.The most pronounced negative impact on vegetation cover was caused by summer air and soil temperatures,spring temperature,and summer winds,and the positive impact was caused by summer precipitation and soil moisture.The response of the vegetation cover to climate was non-uniform concerning the topography.Thus,a significant correlation with the amount of precipitation was observed for~20%–35%of vegetation growing below 600 m above sea level and for less than 5%above this elevation.The negative effect of summer temperatures on plants prevailed mainly at an elevation below~1400 m above sea level.Projected climate change is likely to lead to significant degradation of vegetation in the steppe and foreststeppe in Khakassia in the coming decades.

Discontinuity development patterns and the challenges for 3D discrete fracture network modeling on complicated exposed rock surfaces

Natural slopes usually display complicated exposed rock surfaces that are characterized by complex and substantial terrain undulation and ubiquitous undesirable phenomena such as vegetation cover and rockfalls.This study presents a systematic outcrop research of fracture pattern variations in a complicated rock slope,and the qualitative and quantitative study of the complex phenomena impact on threedimensional(3D)discrete fracture network(DFN)modeling.As the studies of the outcrop fracture pattern have been so far focused on local variations,thus,we put forward a statistical analysis of global variations.The entire outcrop is partitioned into several subzones,and the subzone-scale variability of fracture geometric properties is analyzed(including the orientation,the density,and the trace length).The results reveal significant variations in fracture characteristics(such as the concentrative degree,the average orientation,the density,and the trace length)among different subzones.Moreover,the density of fracture sets,which is approximately parallel to the slope surface,exhibits a notably higher value compared to other fracture sets across all subzones.To improve the accuracy of the DFN modeling,the effects of three common phenomena resulting from vegetation and rockfalls are qualitatively analyzed and the corresponding quantitative data processing solutions are proposed.Subsequently,the 3D fracture geometric parameters are determined for different areas of the high-steep rock slope in terms of the subzone dimensions.The results show significant variations in the same set of 3D fracture parameters across different regions with density differing by up to tenfold and mean trace length exhibiting differences of 3e4 times.The study results present precise geological structural information,improve modeling accuracy,and provide practical solutions for addressing complex outcrop issues.

Analysis on the Change of Vegetation Cover and Its Prediction Method——A Case Study in Eastern Jilin

[Objective] This study aimed to investigate the dynamic changes of vegetation cover and its prediction method. [Method] The NDVl was used as data source to perform the spatial overlay analysis on the vegetation coverage changes of the study area in different time period under the GIS platform, with the aim to reveal the spatial distribution rules of the vegetation cover in Eastern Jilin Province during the recent 10 years. The Markov Model and Grey System G （1, 1） theory model were used to predict the vegetation cover change trend in Eastern Jilin Province. [Result] The vegetation cover increased a little, but staying stable in general. The regions with great changes were mainly around the lake and river. The prediction results of Markov Model and Grey System G （1, 1） theory model were consistent with the observed measurement. [Conclusion] This study provided referential basis for the effective protection of the vegetation coverage in mountainous forest, and important reference value for the scientific decision-making on the forest construction planning in Jilin Province as well as in China and sustainable development of social economy.

The Influence of Vegetation Cover on Summer Precipitation in China: a Statistical Analysis of NDVI and Climate Data

This study provides new evidence for the feedback effects of vegetation cover on summer precipitation in different regions of China by calculating immediate (same season), and one-and two-season lagged correlations between the normalized difference vegetation index (NDVI) and summer precipitation. The results show that the correlation coefficients between NDVI in spring and the previous winter and precipitation in summer are positive in most regions of China, and they show significant difference between regions. The stronger one-and two-season lagged correlations occur in the eastern arid/semi-arid region, Central China, and Southwest China out of the eight climatic regions of China, and this implies that vegetation cover change has more sensitive feedback effects on summer precipitation in the three regions. The three regions are defined as sensitive regions. Spatial analyses of correlations between spring NDVI averaged over each sensitive region and summer precipitation of 160 stations suggest that the vegetation cover strongly affects summer precipitation not only over the sensitive region itself but also over other regions, especially the downstream region.

Vegetation cover change and the driving factors over northwest China

In this paper the spatio-temporal variation of vegetation cover in northwest China during the period of 1982-2006 and its driving factors were analyzed using GIMMS/NDVI data. The annual average NDVI was increased with a rate of 0.0005/a in northwest China and there was an obvious difference between regions. The trend line slopes of NDVI were higher than 0.0005 in the Tianshan Moutains and Altay Mountains of Xinjiang, the Qilian Mountains of Gansu and the eastern part of Qinghai, which indicated the vegetation cover was significantly increased in these areas. The trend line slopes of NDVI were lower than -0.0005 in the southern region of Qinghai, the border regions of Shaanxi and Ningxia, the parts of Gansu and Tarim Basin, Turpan and Tuoli in Xinjiang, which indicated the vegetation cover was declined in these areas. The NDVI of woodland, grassland and cultivated land had an ascending tendency during the study period. The study shows that the vegetation cover change was caused by both natural factors and human activities in northwest China. The natural vegetation change, such as forests was influenced by climate change, while human activities were the main reason to the change of planting vegetation. The changes of vegetation covers for different elevations, slopes and slope aspects were quite different. When the eleva- tion is exceeded to 4,000 m, the NDVI increasing trend was very low; the NDVI at the slope of less than 25~ was increased by the ecological construction; the variation of NDVI on sunny slope was stronger than that on shady slope. The temperature rose significantly in recent 25 years in northwest China by an average rate of 0.67^-C/10a, and precipitation increased by an average rate of 8.15 mm/10a after 1986. There was positive correlation between vegetation cover and temperature and annual precipitation changes. Rising temperature increased the evaporation and drought of soils, which is not conducive to plant growth, and the irrigation in agricultural areas reduced the correlation between agricultural vegetation NDVI and precipita- tion. The improvement of agricultural production level and the projects of ecological construction are very important causes for the NDVI increase in northwest China, and the ecological effect of large-scale ecological construction projects has appeared.

Spatial and temporal variations of vegetation cover and the relationships with climate factors in Inner Mongolia based on GIMMS NDVI3g data

Variation in vegetation cover in Inner Mongolia has been previously studied by the remote sensing data spanning only one decade. However, spatial and temporal variations in vegetation cover based on the newly released GIMMS NDVI3g data spanning nearly thirty years have yet to be analyzed. In this study, we applied the methods of the maximum value composite （MVC） and Pearson＇s correlation coefficient to analyze the variations of vegetation cover in Inner Mongolia based on GIMMS NDVI3g data spanning from 1982 to 2013. Our results indicate that the normalized difference vegetation index （NDVI） increased at a rate of 0.0003/a during the growing seasons despite of the drier and hotter climate in Inner Mongolia during the past three decades. We also found that vegetation cover in the southern agro-pastoral zone significantly increased, while it significantly decreased in the central Alxa. The variations in vegetation cover were not significant in the eastern and central regions. NDVI is positively correlated with precipitation （r=0.617, P=0.000） and also with air temperature （r=0.425, P=0.015）, but the precipitation had a greater effect than the air temperature on the vegetation variations in Inner Mongolia.

Seasonal variations in the influence of vegetation cover on soil water on the loess hillslope

Soil water is the key factor that restricts the restoration of the local ecological systems in the Loess Plateau of China.Studying the effects of vegetation types on soil water and its seasonal variation helps to understand hydrological characteristics and provides insights into the sustainable restoration of vegetation.Therefore,the Caijiachuan watershed was chosen as the research object to investigate the water status of a 0-10 m soil layer under different vegetation types including Pinus tabulaeformis,Robinia pseudoacacia,Platycladus orientalis,apple orchard,natural forestland,farmland and grassland.By comparing the difference between soil water of different land use types and that of grassland during the same period,the seasonal changes of soil water status of different types were judged.The results show that(1)in the 0-10 m soil layer,the largest value of soil water content was in the0.3-0.4 m layer,and the lowest was in the 5.6-5.8 m layer.The depths at which the vegetation cover influenced the soil water were up to 10 m;(2)among summer,fall and spring,the soil water storage wasthe highest in the fall.In addition,the lowest value of relative accumulation was in the fall,which was the period in which the soil water recovered;(3)the soil water in the 0-10 m layer was in a relatively deficient state in the artificial forestlands,apple orchards and native forestlands,while the relative accumulation was in the farmland.In addition,the relative deep soil layers(8-10 m)had more serious deficits in the areas in which P.tabulaeformis,R.pseudoacacia and the apple orchard grew;(4)during the study period,the farmland in the summer had the largest relative accumulation(182.71 mm),and the land under R.pseudoacacia in the fall had the lowest relative deficit(512.20 mm).In the Loess Plateau,vegetation cover will affect the change of deep soil moisture and artificial forest will cause soil water loss in different degrees.

Responses of vegetation cover to the Grain for Green Program and their driving forces in the He-Long region of the middle reaches of the Yellow River

The implementation of the Grain for Green Program is a great breakthrough in the history of China＇s ecological environment construction,which can control soil erosion effectively,increase land productivity and improve the ecological environment.To investigate the eco-environmental benefits brought by the Grain for Green Program,the spatiotemporal variations of vegetation cover in the growing season from 2000 to 2010 across the Hekou-Longmen （He-Long） region were analyzed by using remote sensing information,meteorological data and land use data.Moreover,the impacts of climate and human activities on vegetation change were evaluated objectively.Annual vegetation cover in the growing season increased very significantly.Increased vegetation cover occurred in 98.7％ of the region,of which the area for vegetation cover improved slightly constituted 79.8％ of the whole area.Vegetation moderately improved was mainly distributed in the south of the He-Long region,covering 9.6％ of the area,and the area for vegetation basically unchanged concentrated in the middle and upper reaches of the Wuding River.Precipitation was found to be an important natural factor influencing vegetation cover change.The area of vegetation cover showing a significantly positive correlation with precipitation occupied 22.14％ of the region.As driven by policies from the Grain for Green Program,forestland increased significantly and land use structure became more intensive.Human activities played a positive and effective role in the protection,restoration and improvement of vegetation in the places where vegetation cover was basically unchanged,even though precipitation declined greatly,and vegetation improved moderately with massive increases of forestland and grassland.

Dynamic change of net primary productivity and fractional vegetation cover in the Yellow River Basin using multi-temporal AVHRR NDVI Data

An exponential relationship between net primary productivity (NPP) and integrated NDVI has been found in this paper. Based on the relationship and using multi-temporal 8 km resolution NOAA AVHRR-NDVI data, the spatial distribution and dynamic change of NPP and fractional vegetation cover in the Yellow River Basin from 1982 to 1999 are analyzed. Finally, the effect of rainfall on NDVI is examined. Results show that mean NPP and fractional vegetation cover have an inclining trend for the whole basin, and rainfall in flood season influences vegetation cover most.

AN EXPERIMENTAL STUDY OF INFILTRATION AND EROSION UNDER SLOPE GRADIENTS AND VEGETAL COVERS

The effect of slope gradient and vegetal cover on soil infiltration and erosion were studied with field simulator. Results showed that infiltration decreases with slope gradients (especially for gradient less than 10°) and increases with vegetal covers, while soil erosion increases with slope gradients and decreases with vegetal covers. Fittlng the data by Philip's infiltration equation it was found that in the equation, gravitational conductivity decreases with gradient and increases with vegetation, while diffusion decreases with vegetal cover and does not vary with slope gradient In the erosion process, the formation of a layer of thin water is crucial in dctermining the rainsplash and sheetwash. The increasing of erosion with slope gradient contributes mainly to the increase of velocity.

Vegetation cover density and disturbance affected arbuscular mycorrhiza fungi spore density and root colonization in a dry Afromontane forest, northern Ethiopia

Arbuscular mycorrhiza fungi(AMF) are vital in the regeneration of vegetation in disturbed ecosystems due to their numerous ecological advantages and therefore are good indicators of soil and ecosystem health at large. This study was aimed at determining how the seasonal, vegetation cover density, edaphic and anthropogenic factors affect AMF root colonization(RC) and spore density(SD)in Desa’a dry Afromontane forest. AMF RC and SD in the rhizosphere of five dominant woody species, Juniperus procera, Olea europaea, Maytenus arbutifolia, Carissa spinarum and Dodonaea angustifolia growing in Desa’a forest were studied during the rainy and the dry seasons in three permanent study vegetation cover density plots(dense, medium, and poor). Each plot(160 x40 m2) has two management practices(fenced and unfenced plots) of area. A 100 g sample of rhizosphere soil from moisturefree composite soil was used to determine spore density.Spore density ranged from 50 to 4467 spores/100 g soil,and all species were colonized by AMF within a range of 4–95%. Glomus was the dominant genus in the rhizosphere of all species. Vegetation cover density strongly affected SD and RC. The SD was significantly higher(p < 0.05) in the poor vegetation cover density than in the other two and lowest in the dense cover; root colonization showed the reverse trend. Management practices significantly(p <0.05) influenced AMF SD and RC, with the fenced plots being more favoured. Seasons significantly(p < 0.05) affected RC and SD. More RC and SD were observed in the wet period than the dry period. Correlating AMF SD and RC with soil physical and chemical properties showed no significant difference(p> 0.05) except for total nitrogen. Disturbance, vegetation cover density, season and total nitrogen are significant factors that control the dynamics and management interventions to maintain the forest health of dry Afromontane forests.

Estimating wheat fractional vegetation cover using a density peak k-means algorithm based on hyperspectral image data

Fractional vegetation cover(FVC)is an important parameter to measure crop growth.In studies of crop growth monitoring,it is very important to extract FVC quickly and accurately.As the most widely used FVC extraction method,the photographic method has the advantages of simple operation and high extraction accuracy.However,when soil moisture and acquisition times vary,the extraction results are less accurate.To accommodate various conditions of FVC extraction,this study proposes a new FVC extraction method that extracts FVC from a normalized difference vegetation index(NDVI)greyscale image of wheat by using a density peak k-means(DPK-means)algorithm.In this study,Yangfumai 4(YF4)planted in pots and Yangmai 16(Y16)planted in the field were used as the research materials.With a hyperspectral imaging camera mounted on a tripod,ground hyperspectral images of winter wheat under different soil conditions(dry and wet)were collected at 1 m above the potted wheat canopy.Unmanned aerial vehicle(UAV)hyperspectral images of winter wheat at various stages were collected at 50 m above the field wheat canopy by a UAV equipped with a hyperspectral camera.The pixel dichotomy method and DPK-means algorithm were used to classify vegetation pixels and non-vegetation pixels in NDVI greyscale images of wheat,and the extraction effects of the two methods were compared and analysed.The results showed that extraction by pixel dichotomy was influenced by the acquisition conditions and its error distribution was relatively scattered,while the extraction effect of the DPK-means algorithm was less affected by the acquisition conditions and its error distribution was concentrated.The absolute values of error were 0.042 and 0.044,the root mean square errors(RMSE)were 0.028 and 0.030,and the fitting accuracy R2 of the FVC was 0.87 and 0.93,under dry and wet soil conditions and under various time conditions,respectively.This study found that the DPK-means algorithm was capable of achieving more accurate results than the pixel dichotomy method in various soil and time conditions and was an accurate and robust method for FVC extraction.

Spatial and temporal variability in vegetation cover of Mongolia and its implications

In this paper, we attempted to determine the most stable or unstable regions of vegetation cover in Mongolia and their spatio-temporal dynamics using Terra/MODIS Normalized Difference Vegetation Index （NDVI） dataset, which had a 250-m spatial resolution and comprised 6 periods of 16-day composited temporal resolution data （from 10 June to 13 September） for summer seasons from 2000 to 2012. We also used precipitation data as well as biomass data from 12 meteorological stations located in 4 largest natural zones of Mongolia. Our study showed that taiga and forest steppe zones had relatively stable vegetation cover because of forest characteristics and relatively high precipitation. The highest coefficient of variation （CV） of vegetation cover occurred frequently in the steppe and desert steppe zones, mainly depending on variation of precipitation. Our results showed that spatial and temporal variability in vegetation cover （NDVI or plant biomass） of Mongolia was highly dependent on the amount, distribution and CV of precipitation. This suggests that the lowest inter-annual CV of NDVI can occur dur- ing wet periods of growing season or in high precipitation regions, while the highest inter-annual CV of NDVI can occur during dry periods and in low precipitation regions. Although the desert zone received less precipitation than other natural zones of the country, it had relatively low variation compared to the steppe and desert steppe, which could be attributed to the very sparse vegetation in the desert.

Estimation of Fractional Vegetation Cover Based on Digital Camera Survey Data and a Remote Sensing Model

The objective of this paper is to improve the monitoring speed and precision of fractional vegetation cover (fc). It mainly focuses on fc estimation when fcmax and fcmin are not approximately equal to 100% and 0%, respectively due to using remote sensing image with medium or low spatial resolution. Meanwhile, we present a new method of fc estimation based on a random set of fc maximum and minimum values from digital camera (DC) survey data and a di- midiate pixel model. The results show that this is a convenient, efficient and accurate method for fc monitoring, with the maximum error -0.172 and correlation coefficient of 0.974 between DC survey data and the estimated value of the remote sensing model. The remaining DC survey data can be used as verification data for the precision of the fc estimation. In general, the estimation of fc based on DC survey data and a remote sensing model is a brand-new development trend and deserves further extensive utilization.

Spatial-temporal Changes of Vegetation Cover in Guizhou Province, Southern China

Guizhou Province is an important karst area in the world and a fragile ecological area in China. Ecological risk assessment is very necessary to be conducted in this region. This study investigates different characteristics of the spatial-temporal changes of vegetation cover in Guizhou Province of Southern China using the data set of SPOT VEGETATION(1999–2015) at spatial resolution of 1-km and temporal resolution of 10-day. The coefficient of variation, the Theil-Sen median trend analysis, and the Mann-Kendall test are used to investigate the spatial-temporal change of vegetation cover and its future trend. Results show that: 1) the spatial distribution pattern of vegetation cover in Guizhou Plateau is high in the east whereas low in the west. The average annual normalized difference vegetation index(NDVI) from west to east is higher than that from south to north. 2) Average annual NDVI improved obviously in the past 17 years. The growth rate of average annual NDVI is 0.028/10 yr, which is slower than that of vegetation in the country(0.048/10 yr) from 1998 to 2007. Average annual NDVI in karst area is lower than that in non-karst area. However, the growing rate of average annual NDVI in karst area(0.030/10 yr) is faster than that in non-karst area(0.023/10 yr), indicating that vegetation coverage increases more rapidly in karst area. 3) Vegetation coverage in the study area is stable overall, but fluctuates in the local scales. 4) Vegetation coverage presents a continuous increasing trend. The Hurst exponent of NDVI in different vegetation types has an obvious threshold in various elevations. 5) The proportion of vegetation cover with sustainable increase is higher than that of vegetation cover with sustainable decrease. The improvement in vegetation cover may expand to most parts of the study area.

Soil and Vegetation Spectral Coupling Difference (SVSCD) for Minerals Extraction from Hyperion Data in Vegetation Covered Area

Remote sensing data have been widely applied to extract minerals in geologic exploration, however, in areas covered by vegetation, extracted mineral information has mostly been small targets bearing little information. In this paper, we present a new method for mineral extraction aimed at solving the difficulty of mineral identification in vegetation covered areas. The method selected six sets of spectral difference coupling between soil and plant(SVSCD). These sets have the same vegetation spectra reflectance and a maximum different reflectance of soil and mineral spectra from Hyperion image based on spectral reflectance characteristics of measured spectra. The central wavelengths of the six, selected band pairs were 2314 and 701 nm, 1699 and 721 nm, 1336 and 742 nm, 2203 and 681 nm, 2183 and 671 nm, and 2072 and 548 nm. Each data set's reflectance was used to calculate the difference value. After band difference calculation, vegetation information was suppressed and mineral abnormal information was enhanced compared to the scatter plot of original band. Six spectral difference couplings, after vegetation inhibition, were arranged in a new data set that requires two components that have the largest eigenvalue difference from principal component analysis(PCA). The spatial geometric structure features of PC1 and PC2 was used to identify altered minerals by spectral feature fitting(SFF). The collecting rocks from the 10 points that were selected in the concentration of mineral extraction were analyzed under a high-resolution microscope to identify metal minerals and nonmetallic minerals. Results indicated that the extracted minerals were well matched with the verified samples, especially with the sample 2, 4, 5 and 8. It demonstrated that the method can effectively detect altered minerals in vegetation covered area in Hyperion image.

The application of very high resolution satellite image in urban vegetation cover investigation:a case study of Xiamen City

With the technological improvements of satellite sensors, we will acquire more information about the earth so that we have reached a new application epoch of observation on earth environmental change and cartography. But with the enhancement of spatial resolution, some questions have arisen in the application of using traditional image processing and classification methods. Aiming for such questions, we studied the application of IKONOS very high resolution image (1 m) in Xiamen City on Urban Vegetation Cover Investigation and discussed the difference between the very high resolution image and traditional low spatial resolution image at classification, information abstraction etc. It is an advantageous test for the large-scale application of very high resolution data in the future.

Long Term Evaluation of Green Vegetation Cover Dynamic in the Atacora Mountain Chain(Togo) and its Relation to Carbon Sequestration in West Africa

The research was done in the Atacora Mountain chain in Togo which tended to assess the change of vegetation cover during a 24-year period.It also aims to evaluate the dynamic of the net primary productivity(NPP) of the living plants over the same period.The Landsat imagery covering three different periods(1987, 2000, and 2011) was pre-processed to correct atmospheric and radiometric parameters as well as gapfilling the 2011 SCL-off images.Then, the vegetation indices such as NDVI(normalized difference vegetation index), SR(simple ratiovegetation index), SAVI(soil-adjusted vegetation index), and CASA(carnegie- ames- stanford approach)model for NPP were applied on these images after masking the study area.The results showed a quiet decrease in the vegetation cover.The vegetation loss was more significant from 2000 to 2011 than from1987 to 2000, and anthropogenic activities can be deemed as the main cause of the vegetation loss.The biomass assessment by NPP computation also showed a decrease over the time.Similar to the change of the vegetation cover, the ecosystem net productivity was very low in 2011 compared to 2000 and 1987.It seems that the general health condition of thevegetation, including its potentiality in carbon sinking,was negatively affected in this area, which has already been under threatened.A perpetual monitoring of these ecosystems by means of efficient techniques could enhance the sustainable management tools of in the framework of reducing emissions from deforestation and forest degradation(REDD).

Spatio-temporal variation of ecosystem service values adjusted by vegetation cover:a case study of Wuyishan National Park Pilot,China

Ecosystem service values(ESV)are strongly influenced by the vegetation cover,which is heterogeneous across different vegetation types.We develop a dynamic evaluation model of ESV for Wuyishan National Park Pilot adjusted by the rate of inflation and the fractional vegetation cover,which is calculated by an enhanced vegetation index from 2000 to 2018.The spatio-temporal variation of vegetation was also examined.The results demonstrated that:(1)the unit area of ecosystem service values adjusted by vegetation cover(ESVVC)shows a gradient of forest>tea plantation>grassland>cropland,and the major ecosystem services provided by forests include soil formation and conservation,climate regulation,and biodiversity maintenance;(2)the ESV_(VC) increased to 2.1 billion yuan(The reference rate announced by the People’s Bank of China is the US dollar to 6.42 Yuan per dollar.)from 2000 to 2018.Higher and lower ESV_(VC) are predominant in the northwest and southeast region,respectively.In addition,changes of ecological protection structures and human disturbances negatively affected vegetation cover,leading to a decreased ESVVC from 2000 to 2005 in the Jiuqu Stream Ecological Protection Area and the Wuyishan National Scenic Spot.The implementation of ecological protection policies from 2010 to 2018 enhanced the ESV_(VC) in the study area;and,(3)the ESVVC is highest in the southeast and 25°–35°area with altitudes of 800–1000 m.Our model can provide timely and helpful information of changes in ESV for use in ecological corridor design and ecological security monitoring.