The Qinghai-Xizang Plateau, or Tibetan Plateau, is a sensitive region for climate change, where the manifestation of global warming is particularly noticeable. The wide climate variability in this region significantly...The Qinghai-Xizang Plateau, or Tibetan Plateau, is a sensitive region for climate change, where the manifestation of global warming is particularly noticeable. The wide climate variability in this region significantly affects the local land ecosystem and could consequently lead to notable vegetation changes. In this paper, the interannual variations of the plateau vegetation are investigated using a 21-year normalized difference vegetation index （NDVI） dataset to quantify the consequences of climate warming for the regional ecosystem and its interactions. The results show that vegetation coverage is best in the eastern and southern plateau regions and deteriorates toward the west and north. On the whole, vegetation activity demonstrates a gradual enhancement in an oscillatory manner during 1982-2002. The temporal variation also exhibits striking regional differences： an increasing trend is most apparent in the west, south, north and southeast, whereas a decreasing trend is present along the southern plateau boundary and in the central-east region. Covariance analysis between the NDVI and surface temperature/precipitation suggests that vegetation change is closely related to climate change. However, the controlling physical processes vary geographically. In the west and east, vegetation variability is found to be driven predominantly by temperature, with the impact of precipitation being of secondary importance. In the central plateau, however, temperature and precipitation factors are equally important in modulating the interannual vegetation variability.展开更多
Due to the fast development of industrialization and urbanization, shorelineextraction is necessary for the sustainable development and environment protection inmany countries. This study focused on the accurate metho...Due to the fast development of industrialization and urbanization, shorelineextraction is necessary for the sustainable development and environment protection inmany countries. This study focused on the accurate methods of extracting theinstantaneous waterline —shoreline obtained as the same instant as the satellite imageis acquired. Based on NDWI (Normalized Difference Water Index) and MNDWI(Modified Normalized Difference Water Index), the study changed the bandcombination and proposed a second modified normalized water index (SMNDWI) toextract the waterline. And, this new index is applied to three types of coast to evaluatethe performance of this method with traditional ones. Results show that SNDWI isbetter than NDWI and suitable for applying to the waterline extraction.展开更多
A spectral reflectance sensor(SRS)fixed on the near-surface ground was developed to support the continuous monitoring of vegetation indices such as the normalized difference vegetation index(NDVI)and photochemical ref...A spectral reflectance sensor(SRS)fixed on the near-surface ground was developed to support the continuous monitoring of vegetation indices such as the normalized difference vegetation index(NDVI)and photochemical reflectance index(PRI).NDVI is useful for indicating crop growth/phenology,whereas PRI was developed for observing physiological conditions.Thus,the seasonal change patterns of NDVI and PRI are two valuable pieces of information in a crop-monitoring system.However,capturing the seasonal patterns is considered challenging because the vegetation index values estimated by the reflection from vegetation are often governed by meteorological conditions,such as solar irradiance and precipitation.Further,unlike growth/phenology,the physiological condition has diurnal changes as well as seasonal characteristics.This study proposed a novel filtering method for extracting the seasonal signals of SRS-based NDVI and PRI in paddy rice,barley,and garlic.First,the measurement accuracy of SRSs was compared with handheld spectrometers,and the R^(2)values between the two devices were 0.96 and 0.81 for NDVI and PRI,respectively.Second,the experimental study of threshold criteria with respect to meteorological variables(i.e.,insolation,cloudiness,sunshine duration,and precipitation)was conducted,and sunshine duration was the most useful one for excluding distorted values of the vegetation indices.After data processing based on sunshine duration,the R^(2)values between the measured vegetation indices and the extracted seasonal signals of vegetation indices increased by approximately 0.002–0.004(NDVI)and 0.065–0.298(PRI)on the three crops,and the seasonal signals of vegetation indices became noticeably improved.This method will contribute to an agricultural monitoring system by identifying the seasonal changes in crop growth and physiological conditions.展开更多
Drought was a severe recurring phenomenon in Iraq over the past two decades due to climate change despite the fact that Iraq has been one of the most water-rich countries in the Middle East in the past.The Iraqi Kurdi...Drought was a severe recurring phenomenon in Iraq over the past two decades due to climate change despite the fact that Iraq has been one of the most water-rich countries in the Middle East in the past.The Iraqi Kurdistan Region(IKR)is located in the north of Iraq,which has also suffered from extreme drought.In this study,the drought severity status in Sulaimaniyah Province,one of four provinces of the IKR,was investigated for the years from 1998 to 2017.Thus,Landsat time series dataset,including 40 images,were downloaded and used in this study.The Normalized Difference Vegetation Index(NDVI)and the Normalized Difference Water Index(NDWI)were utilized as spectral-based drought indices and the Standardized Precipitation Index(SPI)was employed as a meteorological-based drought index,to assess the drought severity and analyse the changes of vegetative cover and water bodies.The study area experienced precipitation deficiency and severe drought in 1999,2000,2008,2009,and 2012.Study findings also revealed a drop in the vegetative cover by 33.3%in the year 2000.Furthermore,the most significant shrinkage in water bodies was observed in the Lake Darbandikhan(LDK),which lost 40.5%of its total surface area in 2009.The statistical analyses revealed that precipitation was significantly positively correlated with the SPI and the surface area of the LDK(correlation coefficients of 0.92 and 0.72,respectively).The relationship between SPI and NDVI-based vegetation cover was positive but not significant.Low precipitation did not always correspond to vegetative drought;the delay of the effect of precipitation on NDVI was one year.展开更多
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 influ...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.展开更多
Soil moisture has been considered as one of the main indicators that are widely used in the fields of hydrology, climate, ecology and others. The land surface temperature-vegetation index （LST-VI） space has comprehe...Soil moisture has been considered as one of the main indicators that are widely used in the fields of hydrology, climate, ecology and others. The land surface temperature-vegetation index （LST-VI） space has comprehensive information of the sensor from the visible to thermal infrared band and can well reflect the regional soil moisture conditions. In this study, 9 pairs of moderate-resolution imaging spectroradiometer （MODIS） products （MOD09A1 and MODllA2）, covering 5 provinces in Southwest China, were chosen to construct the LST-VI space, and then the spatial distribution of soil moisture in 5 provinces of Southwest China was monitored by the temperature vegetation dryness index （TVDI）. Three LST-VI spaces were constructed by normalized difference vegetation index （NDVI）, enhanced vegetation index （EVI）, and modified soil-adjusted vegetation index （MSAVI）, respectively. The correlations between the soil moisture data from 98 sites and the 3 TVDIs calculated by LST-NDVI, LST-EVI and LST-MSAVI, respectively, were analyzed. The results showed that TVDI was a useful parameter for soil surface moisture conditions. The TVDI calculated from the LST-EVI space （TVDIE） revealed a better correlation with soil moisture than those calculated from the LST-NDVI and LST-MSAVI spaces. From the different stages of the TVDIE space, it is concluded that TVDIE can effectively show the temporal and spatial differences of soil moisture, and is an effective approach to monitor soil moisture condition.展开更多
The Thornthwaite moisture index, an index of the supply of water （precipitation） in an area relative to the climatic demand for water （potential evapotranspiration）, was used to examine the spatial and temporal va...The Thornthwaite moisture index, an index of the supply of water （precipitation） in an area relative to the climatic demand for water （potential evapotranspiration）, was used to examine the spatial and temporal variation of drought and to verify the influence of environmental factors on the drought in the Hengduan Mountains, China. Results indicate that the Thornthwaite moisture index in the Hengduan Mountains had been increasing since 1960 with a rate of 0.1938/yr. Annual Thomthwaite moisture index in Hengduan Mountains was between -97.47 and 67.43 and the spatial heterogeneity was obvious in different seasons. Thomthwaite moisture index was high in the north and low in the south, and the monsoon rainfall had a significant impact on its spatial distribution. The tendency rate of Thomthwaite moisture index variation varied in different seasons, and the increasing trends in spring were greater than that in summer and autumn. However, the Thomthwaite moisture index decreased in winter. Thomthwaite moisture index increased greatly in the north and there was a small growth in the south of Hengduan Mountains. The increase of precipitation and decrease of evaporation lead to the increase of Thomthwaite moisture index. Thornthwaite moisture index has strong correlation with vegetation coverage. It can be seen that the correlation between Normalized Difference Vegetation Index （NDVI） and Thomthwaite moisture index was positive in spring and summer, but negative in autumn and winter. Correlation between Thornthwaite moisture index and relative soil relative moisture content was positive in spring, summer and autumn, but negative in winter. The typical mountainous terrain affect the distribu- tion of temperature, precipitation, wind speed and other meteorological factors in this region, and then affect the spatial distribution of Thomthwaite moisture index. The unique ridge-gorge terrain caused the continuity of water-heat distribution from the north to south, and the water-heat was stronger than that from the east to west part, and thus determined the spatial distribution of Thornthwaite mois- ture index. The drought in the Hengduan Mountains area is mainly due to the unstable South Asian monsoon rainfall time.展开更多
The effect of global climate change on vegetation growth is variable.Timely and effective monitoring of vegetation drought is crucial for understanding its dynamics and mitigation,and even regional protection of ecolo...The effect of global climate change on vegetation growth is variable.Timely and effective monitoring of vegetation drought is crucial for understanding its dynamics and mitigation,and even regional protection of ecological environments.In this study,we constructed a new drought index(i.e.,Vegetation Drought Condition Index(VDCI))based on precipitation,potential evapotranspiration,soil moisture and Normalized Difference Vegetation Index(NDVI)data,to monitor vegetation drought in the nine major river basins(including the Songhua River and Liaohe River Basin,Haihe River Basin,Yellow River Basin,Huaihe River Basin,Yangtze River Basin,Southeast River Basin,Pearl River Basin,Southwest River Basin and Continental River Basin)in China at 1-month–12-month(T1–T12)time scales.We used the Pearson's correlation coefficients to assess the relationships between the drought indices(the developed VDCI and traditional drought indices including the Standardized Precipitation Evapotranspiration Index(SPEI),Standardized Soil Moisture Index(SSMI)and Self-calibrating Palmer Drought Severity Index(scPDSI))and the NDVI at T1–T12 time scales,and to estimate and compare the lag times of vegetation response to drought among different drought indices.The results showed that precipitation and potential evapotranspiration have positive and major influences on vegetation in the nine major river basins at T1–T6 time scales.Soil moisture shows a lower degree of negative influence on vegetation in different river basins at multiple time scales.Potential evapotranspiration shows a higher degree of positive influence on vegetation,and it acts as the primary influencing factor with higher area proportion at multiple time scales in different river basins.The VDCI has a stronger relationship with the NDVI in the Songhua River and Liaohe River Basin,Haihe River Basin,Yellow River Basin,Huaihe River Basin and Yangtze River Basin at T1–T4 time scales.In general,the VDCI is more sensitive(with shorter lag time of vegetation response to drought)than the traditional drought indices(SPEI,scPDSI and SSMI)in monitoring vegetation drought,and thus it could be applied to monitor short-term vegetation drought.The VDCI developed in the study can reveal the law of unclear mechanisms between vegetation and climate,and can be applied in other fields of vegetation drought monitoring with complex mechanisms.展开更多
Variations in vegetation are closely related to climate change, but understanding of their characteristics and causes remains limited. As a typical semi-humid and semi-arid cold plateau region, it is important to unde...Variations in vegetation are closely related to climate change, but understanding of their characteristics and causes remains limited. As a typical semi-humid and semi-arid cold plateau region, it is important to understand the knowledge of long term Normalized Difference Vegetation Index(NDVI) variations and find the potential causes in the source region of the Yangtze River. Based on four tree-ring width chronologies, the regional mean NDVI for July and August spanning the period 1665–2013 was reconstructed using a regression model, and it explained 43.9% of the total variance during the period 1981–2013. In decadal, the reconstructed NDVI showed eight growth stages(1754–1764, 1766–1783, 1794–1811, 1828–1838, 1843–1855, 1862–1873, 1897–1909, and 1932–1945)and four degradation stages(1679–1698, 1726–1753, 1910–1923, and 1988–2000). And based on wavelet analysis, significant cycles of2–3 yr and 3–8 yr were identified. In additional, there was a significant positive correlation between the NDVI and the Palmer Drought Severity Index(PDSI) during the past 349 yr, and they were mainly in phase. However, according to the results of correlation analysis between different grades of drought/wet and NDVI, there was significant asymmetry in extreme drought years and extreme wet years. In extreme drought years, NDVI was positively correlated with PDSI, and in extreme wet years they were negatively correlated.展开更多
This study describes the spatial and temporal variation of a drought index and makes inferences regarding the environmental factors that influence this variability in the Hengduan Mountains. A drought index is typical...This study describes the spatial and temporal variation of a drought index and makes inferences regarding the environmental factors that influence this variability in the Hengduan Mountains. A drought index is typically used to determine the moisture conditions and the magnitude of water deficiency in a given area. Based on data from 26 meteorological stations over the period 1960-2012, the spatial and temporal variations of the drought index were analyzed using a thin plate smoothing splines method that considered elevation as a covariate. The drought index was estimated based on the potential evapotranspiration(E0) as defined by the Penman Monteith model modified by FAO(1998). The results of the reported analysis showed that the drought index in the Hengduan Mountains has been decreasing since 1960 at a rate of-0.008/a. This represented a progressive shift from the "sub-humid" class, which typified the wider area in the Hengduan Mountains, toward the "humid" class, which appeared in the Hengduan Mountains areas. The drought index was relatively high in the north and low in the south and the variation of the drought index varied with seasons. The drought index showed increasing trends in summer and autumn and it is greater in autumn than in summer, while it showed a decreasing trend in spring and winter. Drought index is inversely proportional to the soil relative humidity and Normalized Difference Vegetation Index(NDVI).展开更多
Mapping rice cropping systems with optical imagery in multiple cropping regions is challenging due to cloud contamination and data availability; development of a phenology-based algorithm with a reduced data demand is...Mapping rice cropping systems with optical imagery in multiple cropping regions is challenging due to cloud contamination and data availability; development of a phenology-based algorithm with a reduced data demand is essential. In this study, the Landsat-derived Renorma- lized Index of Normalized Difference Vegetation Index （RNDVI） was proposed based on two temporal windows in which the NDVI values of single and early （or late） rice display inverse changes, and then applied to discriminate rice cropping systems. The Poyang Lake Region （PLR）, characterized by a typical cropping system of single cropping rice （SCR, or single rice） and double cropping rice （DCR, including early rice and late rice）, was selected as a testing area. The results showed that NDVI data derived from Landsat time-series at eight to sixteen days captures the temporal development of paddy rice. There are two key phenological stages during the overlapping growth period in which the NDVI values of SCR and DCR change inversely, namely the ripening phase of early rice and the growing phase of single rice as well as the ripening stage of single rice and the growing stage of late rice. NDVI derived from scenes in two temporal windows, specifically early August and early October, was used to construct the RNDVI for discriminating rice cropping systems in the polder area of the PLR, China. Comparison with ground truth data indicates high classification accuracy. The RNDVI approach highlights the inverse variations of NDVI values due to the difference of rice growth between two temporal windows. This makes the discrimination of rice cropping systems straightforward as it only needs to distinguish whether the candidate rice typeis in the period of growth （RNDVI 〈 0） or senescence （RNDVI 〉 0）.展开更多
Soil carbon stock research has gained prominence in environmental studies amidst climate change concerns,especially given that soil is one of the largest terrestrial carbon reserves.Accurate predictions necessitate co...Soil carbon stock research has gained prominence in environmental studies amidst climate change concerns,especially given that soil is one of the largest terrestrial carbon reserves.Accurate predictions necessitate comprehensive soil profile measurements,which are resource-intensive to obtain.To address this,depth functions are employed to derive continuous estimates,aligning with standardized depths.However,global datasets employing depth functions in raster format have not been widely utilized,which could lower financial costs and improve accuracy in data-scarce regions.Furthermore,research into aggregating depth functions for realistic carbon stock estimations remains limited,offering opportunities to streamline cost and time.The aim of this study was to apply equal-area splines to estimate soil carbon stocks,utilizing SoilGrids and iSDAsoil datasets in a 317-km^(2) Quaternary catchment(30°48′E,29°18′S)in KwaZulu-Natal,South Africa.Both datasets were resampled to a 250-m resolution,and the splines were interpolated to a depth of 50 cm per pixel.Various aggregation methods were employed in calculation,including the cumulative sum(definite integral),discrete sum(sum of 1-cm spline predictions),and the mean carbon stock(mean to 50 cm).Quantitative evaluation was performed with 310 external soil samples.SoilGrids showed higher predictions(100–546 kg m^(-2))than iSDAsoil(66.9–225 kg m^(-2))for the cumulative sum.The discrete sum also exhibited higher prediction values for SoilGrids(293–789 kg m^(-2))compared to iSDAsoil(228–557 kg m^(-2)).SoilGrids aggregated with the discrete sum closely matched previous studies,estimating total carbon stock for the catchment at 7126 t,albeit with spatial inconsistencies.However,when evaluating with an external dataset,the results were not satisfactory for any method according to Lin's concordance correlation coefficient(CCC,correlation of a 1:1 line),with all models obtaining a CCC below 0.01.Similarly,all models had a root mean squared error larger than 59 kg m^(-2).It was concluded that SoilGrids and iSDAsoil were spatially inaccurate in the catchment but can still provide information about the total carbon stock.This method could be improved by obtaining more soil samples for the datasets,incorporating local data into the spline,making the method more computationally efficient,and accounting for discrete horizon boundaries.展开更多
Understanding the complex relationship between vegetation change and both natural and anthropogenic factors is a subject of global importance.However,comprehensive explanations of vegetation cover trends across China...Understanding the complex relationship between vegetation change and both natural and anthropogenic factors is a subject of global importance.However,comprehensive explanations of vegetation cover trends across China’s different regions and the dynamic roles of their drivers remain limited.This study analyzed national and regional vegetation change trends from 2000 to 2020 and evaluated the evolving impacts of natural and anthropogenic factors.Results indicate that 44.14%of China’s land experienced significant increase(P<0.05)in vegetation coverage.The Northeast(A1),Southwest(A5),and South China(A8)regions showed extremely significant increases in vegetation cover,with over 65%of vegetation exhibiting extremely significant growth(P<0.01).In contrast,less than 25%of vegetation in Inner Mongolia(A2),Northwest(A3),and the Qinghai-Tibetan Plateau(A4)subregions demonstrated an extremely significant increasing trend(P<0.01).Precipitation(q=0.766)and land use type(q=0.636)were the most influential natural and anthropogenic factors,respectively,with their interaction(q=0.838)dominating national vegetation patterns.On the west side of the Hu Line,vegetation dynamics were mainly limited by arid and semi-arid climates,with precipitation as the dominant factor,though land use measures have contributed to some vegetation improvement.Between 2000 and 2020,the influence of precipitation on vegetation cover increased in regions A3 and A4,with q-values rising by 26.73%and 101.13%,respectively.Additionally,soil type exerted a significant effect(P<0.001)on vegetation cover across all regions,being most pronounced in A2(q=0.692).On the east side of the Hu Line,vegetation growth benefited generally from warm and humid conditions,while local decline in urbanized areas was largely attributable to land use change and economic expansion.Concurrently anthropogenic factors such as land use and population density increasingly influenced vegetation dynamics in eastern urban areas of the Hu Line.Population density and GDP were the most influential factors affecting vegetation cover in region A8,with q-values of 0.443 and 0.380,respectively(P<0.001).Future efforts should maintain the benefits of large-scale ecological projects and harmonize the relationship between urban vegetation and anthropogenic influences.展开更多
Climate change in High Mountain Asia(HMA)is characterized by elevation dependence,which results in vertical zoning of vegetation distribution.However,few studies have been conducted on the distribution patterns of veg...Climate change in High Mountain Asia(HMA)is characterized by elevation dependence,which results in vertical zoning of vegetation distribution.However,few studies have been conducted on the distribution patterns of vegetation,the response of vegetation to climate change,and the key climatic control factors of vegetation along the elevation gradient in this region.In this study,based on the Normalized Difference Vegetation index(NDVI),we investigated the evolution pattern of vegetation in HMA during 2001-2020 using linear trend and Bayesian Estimator of Abrupt change,Seasonality,and Trend(BEAST)methods.Pearson correlation analysis and partial correlation analysis were used to explore the response relationship between vegetation and climatic factors along the elevation gradient.Path analysis was employed to quantitatively reveal the dominant climatic factors affecting vegetation distribution along the elevation gradient.The results showed that NDVI in HMA increased at a rate of 0.011/10a from 2001 to 2020,and the rate of increase abruptly slowed down after 2017.NDVI showed a fluctuating increase at elevation zones 1-2(<2500 m)and then decreased at elevation zones 3-9(2500-6000 m)with the increase of elevation.NDVI was most sensitive to precipitation and temperature at a 1-month lag.With the increase of elevation,the positive response relationship of NDVI with precipitation gradually weakened,while that of NDVI with temperature was the opposite.The total effect coefficient of precipitation(0.95)on vegetation was larger than that of temperature(0.87),indicating that precipitation is the dominant control factor affecting vegetation growth.Spacially,vegetation growth is jointly influenced by precipitation and temperature,but the influence of precipitation on vegetation growth is dominant at each elevation zone.The results of this study contribute to understanding how the elevation gradient effect influences the response of vegetation to climate change in alpine ecosystems.展开更多
Droughts impact forests by influencing various processes such as canopy greenness,tree growth,and reproduction,but most studies have only examined a few of these processes.More comprehensive assessments of forest resp...Droughts impact forests by influencing various processes such as canopy greenness,tree growth,and reproduction,but most studies have only examined a few of these processes.More comprehensive assessments of forest responses to climate variability and water shortages are needed to improve forecasts of post-drought dynamics.Iberian forests are well-suited for evaluating these effects because they experience diverse climatic conditions and are dominated by various conifer and broadleaf species,many of which exhibit masting.We assessed how greenness,evaluated using the normalized difference vegetation index(NDVI),tree radial growth,and seed or cone production responded to drought in five tree species(three conifers:silver fir(Abies alba),Scots pine(Pinus sylvestris),and stone pine(Pinus pinea);two broadleaves:European beech(Fagus sylvatica)and holm oak(Quercus ilex)inhabiting sites with different aridity.We correlated these data with the standardized precipitation evapotranspiration index(SPEI)using the climate window analysis(climwin)package,which identifies the most relevant climate window.Drought constrained growth more than greenness and seed or cone production.Dry conditions led to high seed or cone production in species found in cool,moist sites(silver fir,beech,and Scots pine).We also found negative associations of cone production with summer SPEI in the drought-tolerant stone pine,which showed lagged growth-cone negative correlations.However,in the seasonally dry holm oak forests,severe droughts constrained both growth and acorn production,leading to a positive correlation between these variables.Drought impacts on greenness,growth,seed,and cone production depended on species phenology and site aridity.A negative correlation between growth and reproduction does not necessarily indicate trade-offs,as both may be influenced by similar climatic factors.展开更多
Investigating the spatiotemporal evolution of vegetation and its response mechanisms to natural and anthropogenic elements is crucial for regional vegetation restoration and ecological preservation.The Mu Us Sandy Lan...Investigating the spatiotemporal evolution of vegetation and its response mechanisms to natural and anthropogenic elements is crucial for regional vegetation restoration and ecological preservation.The Mu Us Sandy Land(MUSL),which is situated in the semi-arid zone of northwestern China adjacent to the Loess Plateau,has been at the forefront of desertification and oasis formation over the past two millennia.This study is based on the synthesis of the Normalized Difference Vegetation Index(NDVI)data from MOD13A3 data in the MODIS(Moderate-Resolution Imaging Spectroradiometer)dataset(2002-2021)and climate data(temperature and precipitation)at annual and monthly scales from the National Earth System Science Data Center.A range of analytical methods,including univariate linear regression,Theil-Sen trend analysis and Mann-Kendall significance test,correlation analysis,residual analysis,and Hurst index,were used to explore the response mechanisms of the NDVI to climate change and human activities and to predict the future trends of the NDVI in the MUSL.The results showed that through the method of correlation analysis,in terms of both spatially averaged correlation coefficients and area proportion,the NDVI was positively correlated with temperature and precipitation in 97.59%and 96.51%of the study area,respectively,indicating that temperature has a greater impact on the NDVI than precipitation.Residual analysis quantified the contributions of climate change and human activities to the NDVI changes,revealing that climate change and human activities contribute up to 30.00%and 70.00%,respectively,suggesting that human activities predominantly affect the NDVI changes in the MUSL.The Hurst index was used to categorize the future trend of the NDVI into four main directions of development:continuous degradation(0.05%of the study area),degradation in the past but improvement in the future(54.45%),improvement in the past but degradation in the future(0.13%),and continuous improvement(45.36%).In more than 50.00%of the regions that have been degraded in the past but were expected to improve in the future,the NDVI was expected to exhibit a stable trend of anti-persistent improvement.These findings provide theoretical support for future ecological protection,planning,and the implementation of ecological engineering in the MUSL,and also offer a theoretical basis for the planning and execution of construction projects,environmental protection measures,and the sustainable development of vegetation.展开更多
The Loess Plateau(LP),one of the most ecologically fragile regions in China,is affected by severe soil erosion and environmental degradation.Despite large-scale ecological restoration efforts made by Chinese governmen...The Loess Plateau(LP),one of the most ecologically fragile regions in China,is affected by severe soil erosion and environmental degradation.Despite large-scale ecological restoration efforts made by Chinese government in recent years,the region continues to face significant ecological challenges due to the combined impact of climate change and human activities.In this context,we developed a kernal Remote Sensing Ecological Index(kRSEI)using Moderate Resolution Imaging Spectroradiometer(MODIS)products on the Google Earth Engine(GEE)platform to analyze the spatiotemporal patterns and trends in ecological environmental quality(EEQ)across the LP from 2000 to 2022 and project future trajectories.Then,we applied partial correlation analysis and multivariate regression residual analysis to further quantify the relative contributions of climate change and human activities to EEQ.During the study period,the kRSEI values exhibited significant spatial heterogeneity,with a stepwise degradation pattern in the southeast to northwest across the LP.The maximum(0.51)and minimum(0.46)values of the kRSEI were observed in 2007 and 2021,respectively.Trend analyses revealed a decline in EEQ across the LP.Hurst exponent analysis predicted a trend of weak anti-persistent development in most of the plateau areas in the future.A positive correlation was identified between kRSEI and precipitation,particularly in the central and western regions;although,improvements were limited by a precipitation threshold of 837.66 mm/a.A moderate increase in temperature was shown to potentially benefit the ecological environment within a certain range;however,temperature of-1.00°C-7.95°C often had a negative impact on the ecosystem.Climate change and human activities jointly influenced 65.78%of LP area on EEQ,primarily having a negative impact.In terms of contribution,human activities played a dominant role in driving changes in EEQ across the plateau.These findings provide crucial insights for accurately assessing the ecological state of the LP and suggest the design of future restoration strategies.展开更多
Root zone soil moisture(RZSM)plays a critical role in land-atmosphere hydrological cycles and serves as the primary water source for vegetation growth.However,the correlations between RZSM and its associated variables...Root zone soil moisture(RZSM)plays a critical role in land-atmosphere hydrological cycles and serves as the primary water source for vegetation growth.However,the correlations between RZSM and its associated variables,including surface soil moisture(SSM),often exhibit nonlinearities that are challenging to identify and quantify using conventional statistical techniques.Therefore,this study presents a hybrid convolutional neural network(CNN)-long short-term memory neural network(LSTM)-attention(CLA)model for predicting RZSM.Owing to the scarcity of soil moisture(SM)observation data,the physical model Hydrus-1D was employed to simulate a comprehensive dataset of spatial-temporal SM.Meteorological data and moderate resolution imaging spectroradiometer vegetation characterization parameters were used as predictor variables for the training and validation of the CLA model.The results of the CLA model for SM prediction in the root zone were significantly enhanced compared with those of the traditional LSTM and CNN-LSTM models.This was particularly notable at the depth of 80–100 cm,where the fitness(R^(2))reached nearly 0.9298.Moreover,the root mean square error of the CLA model was reduced by 49%and 57%compared with those of the LSTM and CNN-LSTM models,respectively.This study demonstrates that the integration of physical modeling and deep learning methods provides a more comprehensive and accurate understanding of spatial-temporal SM variations in the root zone.展开更多
As global urbanization accelerates,with the urban population projected to reach 68%by 2050,the impact of urban environments on eye health has emerged as a significant public health issue.From the"global pandemic&...As global urbanization accelerates,with the urban population projected to reach 68%by 2050,the impact of urban environments on eye health has emerged as a significant public health issue.From the"global pandemic"of myopia to the surge in diabetic retinopathy,from visual fatigue induced by blue light exposure to age-related glaucoma and macular degeneration,urban lifestyles are reshaping the epidemiological landscape of ophthalmic diseases.This issue focuses on"Urban Eye Health,"aiming to integrate cutting-edge research findings worldwide and explore how to establish a sustainable eye health management system through technological innovation,policy coordination,and community intervention.展开更多
This research aims to analyse the spatio-temporal changes of vegetation cover in coastal regions of Char Fasson and Galachipa Upazila, Bangladesh for a period of 30 years (1994-2024) based on Landsat satellite imagery...This research aims to analyse the spatio-temporal changes of vegetation cover in coastal regions of Char Fasson and Galachipa Upazila, Bangladesh for a period of 30 years (1994-2024) based on Landsat satellite imagery and NDVI. Through the evaluation of NDVI this paper classifies vegetation as no water/bare vegetation, slightly densed vegetation, moderately densed vegetation, and highly densed vegetation. The findings reveal significant fluctuations in vegetation cover: from 1994 to 2004, there has been an increase in vegetation density implying that afforestation has created more moderate and highly densed vegetation out of density vegetation. However, between 2004 and 2014, vegetation cover decreased because some cyclones, like Sidr and Aila, affected the coastal forest of Bangladesh. Other attempts to afforestation supported improved coverage from vegetation between 2014 and 2024. These findings provide clear evidence of the sustainable benefits of coastal afforestation in the reduction of coastal erosion and storm surges that affect vegetation and coasts. Knowledge gained in this research is highly useful to the environmental planners on recommendations for sustainable land uses and preservation to build up ecological stability in Bangladesh weak coastal areas.展开更多
基金supported by the foundation from:the program of the National Natural Science Foundation of China(40675037)the key program of the Sichuan Province Youth Science and Technology Fund(05ZQ026-023)the opening project of the State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics,Institute of Atmospheric Physics,Chinese Academy of Sciences.
文摘The Qinghai-Xizang Plateau, or Tibetan Plateau, is a sensitive region for climate change, where the manifestation of global warming is particularly noticeable. The wide climate variability in this region significantly affects the local land ecosystem and could consequently lead to notable vegetation changes. In this paper, the interannual variations of the plateau vegetation are investigated using a 21-year normalized difference vegetation index （NDVI） dataset to quantify the consequences of climate warming for the regional ecosystem and its interactions. The results show that vegetation coverage is best in the eastern and southern plateau regions and deteriorates toward the west and north. On the whole, vegetation activity demonstrates a gradual enhancement in an oscillatory manner during 1982-2002. The temporal variation also exhibits striking regional differences： an increasing trend is most apparent in the west, south, north and southeast, whereas a decreasing trend is present along the southern plateau boundary and in the central-east region. Covariance analysis between the NDVI and surface temperature/precipitation suggests that vegetation change is closely related to climate change. However, the controlling physical processes vary geographically. In the west and east, vegetation variability is found to be driven predominantly by temperature, with the impact of precipitation being of secondary importance. In the central plateau, however, temperature and precipitation factors are equally important in modulating the interannual vegetation variability.
基金supported by Tianjin Natural Science Foundation Project (14JCYBJC22500)
文摘Due to the fast development of industrialization and urbanization, shorelineextraction is necessary for the sustainable development and environment protection inmany countries. This study focused on the accurate methods of extracting theinstantaneous waterline —shoreline obtained as the same instant as the satellite imageis acquired. Based on NDWI (Normalized Difference Water Index) and MNDWI(Modified Normalized Difference Water Index), the study changed the bandcombination and proposed a second modified normalized water index (SMNDWI) toextract the waterline. And, this new index is applied to three types of coast to evaluatethe performance of this method with traditional ones. Results show that SNDWI isbetter than NDWI and suitable for applying to the waterline extraction.
基金supported by the Rural Development Administration(PJ013821032020),Republic of Korea。
文摘A spectral reflectance sensor(SRS)fixed on the near-surface ground was developed to support the continuous monitoring of vegetation indices such as the normalized difference vegetation index(NDVI)and photochemical reflectance index(PRI).NDVI is useful for indicating crop growth/phenology,whereas PRI was developed for observing physiological conditions.Thus,the seasonal change patterns of NDVI and PRI are two valuable pieces of information in a crop-monitoring system.However,capturing the seasonal patterns is considered challenging because the vegetation index values estimated by the reflection from vegetation are often governed by meteorological conditions,such as solar irradiance and precipitation.Further,unlike growth/phenology,the physiological condition has diurnal changes as well as seasonal characteristics.This study proposed a novel filtering method for extracting the seasonal signals of SRS-based NDVI and PRI in paddy rice,barley,and garlic.First,the measurement accuracy of SRSs was compared with handheld spectrometers,and the R^(2)values between the two devices were 0.96 and 0.81 for NDVI and PRI,respectively.Second,the experimental study of threshold criteria with respect to meteorological variables(i.e.,insolation,cloudiness,sunshine duration,and precipitation)was conducted,and sunshine duration was the most useful one for excluding distorted values of the vegetation indices.After data processing based on sunshine duration,the R^(2)values between the measured vegetation indices and the extracted seasonal signals of vegetation indices increased by approximately 0.002–0.004(NDVI)and 0.065–0.298(PRI)on the three crops,and the seasonal signals of vegetation indices became noticeably improved.This method will contribute to an agricultural monitoring system by identifying the seasonal changes in crop growth and physiological conditions.
文摘Drought was a severe recurring phenomenon in Iraq over the past two decades due to climate change despite the fact that Iraq has been one of the most water-rich countries in the Middle East in the past.The Iraqi Kurdistan Region(IKR)is located in the north of Iraq,which has also suffered from extreme drought.In this study,the drought severity status in Sulaimaniyah Province,one of four provinces of the IKR,was investigated for the years from 1998 to 2017.Thus,Landsat time series dataset,including 40 images,were downloaded and used in this study.The Normalized Difference Vegetation Index(NDVI)and the Normalized Difference Water Index(NDWI)were utilized as spectral-based drought indices and the Standardized Precipitation Index(SPI)was employed as a meteorological-based drought index,to assess the drought severity and analyse the changes of vegetative cover and water bodies.The study area experienced precipitation deficiency and severe drought in 1999,2000,2008,2009,and 2012.Study findings also revealed a drop in the vegetative cover by 33.3%in the year 2000.Furthermore,the most significant shrinkage in water bodies was observed in the Lake Darbandikhan(LDK),which lost 40.5%of its total surface area in 2009.The statistical analyses revealed that precipitation was significantly positively correlated with the SPI and the surface area of the LDK(correlation coefficients of 0.92 and 0.72,respectively).The relationship between SPI and NDVI-based vegetation cover was positive but not significant.Low precipitation did not always correspond to vegetative drought;the delay of the effect of precipitation on NDVI was one year.
文摘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.
基金Supported by the National Key Technologies Research and Development Program of the Ministry of Science and Technology of China during the 12th Five-Year Plan Period(Nos.2011BAD32B01 and 2012BAH29B02)
文摘Soil moisture has been considered as one of the main indicators that are widely used in the fields of hydrology, climate, ecology and others. The land surface temperature-vegetation index （LST-VI） space has comprehensive information of the sensor from the visible to thermal infrared band and can well reflect the regional soil moisture conditions. In this study, 9 pairs of moderate-resolution imaging spectroradiometer （MODIS） products （MOD09A1 and MODllA2）, covering 5 provinces in Southwest China, were chosen to construct the LST-VI space, and then the spatial distribution of soil moisture in 5 provinces of Southwest China was monitored by the temperature vegetation dryness index （TVDI）. Three LST-VI spaces were constructed by normalized difference vegetation index （NDVI）, enhanced vegetation index （EVI）, and modified soil-adjusted vegetation index （MSAVI）, respectively. The correlations between the soil moisture data from 98 sites and the 3 TVDIs calculated by LST-NDVI, LST-EVI and LST-MSAVI, respectively, were analyzed. The results showed that TVDI was a useful parameter for soil surface moisture conditions. The TVDI calculated from the LST-EVI space （TVDIE） revealed a better correlation with soil moisture than those calculated from the LST-NDVI and LST-MSAVI spaces. From the different stages of the TVDIE space, it is concluded that TVDIE can effectively show the temporal and spatial differences of soil moisture, and is an effective approach to monitor soil moisture condition.
基金Under the auspices of Chinese Postdoctoral Science Foundation(No.2015M570864)Open-ended Fund of State Key Laboratory of Cryosphere Sciences,Chinese Academy of Sciences(No.SKLCS-OP-2014-11)+2 种基金Northwest Normal University Young Teachers Scientific Research Ability Promotion Plan(No.NWNU-LKQN-13-10)National Natural Science Foundation of China(No.41273010,41271133)Major National Research Projects of China(No.2013CBA01808)
文摘The Thornthwaite moisture index, an index of the supply of water （precipitation） in an area relative to the climatic demand for water （potential evapotranspiration）, was used to examine the spatial and temporal variation of drought and to verify the influence of environmental factors on the drought in the Hengduan Mountains, China. Results indicate that the Thornthwaite moisture index in the Hengduan Mountains had been increasing since 1960 with a rate of 0.1938/yr. Annual Thomthwaite moisture index in Hengduan Mountains was between -97.47 and 67.43 and the spatial heterogeneity was obvious in different seasons. Thomthwaite moisture index was high in the north and low in the south, and the monsoon rainfall had a significant impact on its spatial distribution. The tendency rate of Thomthwaite moisture index variation varied in different seasons, and the increasing trends in spring were greater than that in summer and autumn. However, the Thomthwaite moisture index decreased in winter. Thomthwaite moisture index increased greatly in the north and there was a small growth in the south of Hengduan Mountains. The increase of precipitation and decrease of evaporation lead to the increase of Thomthwaite moisture index. Thornthwaite moisture index has strong correlation with vegetation coverage. It can be seen that the correlation between Normalized Difference Vegetation Index （NDVI） and Thomthwaite moisture index was positive in spring and summer, but negative in autumn and winter. Correlation between Thornthwaite moisture index and relative soil relative moisture content was positive in spring, summer and autumn, but negative in winter. The typical mountainous terrain affect the distribu- tion of temperature, precipitation, wind speed and other meteorological factors in this region, and then affect the spatial distribution of Thomthwaite moisture index. The unique ridge-gorge terrain caused the continuity of water-heat distribution from the north to south, and the water-heat was stronger than that from the east to west part, and thus determined the spatial distribution of Thornthwaite mois- ture index. The drought in the Hengduan Mountains area is mainly due to the unstable South Asian monsoon rainfall time.
基金funded by the National Natural Science Foundation of China(52179015,42301024)the Key Technologies Research&Development and Promotion Program of Henan(232102110025)the Cultivation Plan of Innovative Scientific and Technological Team of Water Conservancy Engineering Discipline of North China University of Water Resources and Electric Power(CXTDPY-9).
文摘The effect of global climate change on vegetation growth is variable.Timely and effective monitoring of vegetation drought is crucial for understanding its dynamics and mitigation,and even regional protection of ecological environments.In this study,we constructed a new drought index(i.e.,Vegetation Drought Condition Index(VDCI))based on precipitation,potential evapotranspiration,soil moisture and Normalized Difference Vegetation Index(NDVI)data,to monitor vegetation drought in the nine major river basins(including the Songhua River and Liaohe River Basin,Haihe River Basin,Yellow River Basin,Huaihe River Basin,Yangtze River Basin,Southeast River Basin,Pearl River Basin,Southwest River Basin and Continental River Basin)in China at 1-month–12-month(T1–T12)time scales.We used the Pearson's correlation coefficients to assess the relationships between the drought indices(the developed VDCI and traditional drought indices including the Standardized Precipitation Evapotranspiration Index(SPEI),Standardized Soil Moisture Index(SSMI)and Self-calibrating Palmer Drought Severity Index(scPDSI))and the NDVI at T1–T12 time scales,and to estimate and compare the lag times of vegetation response to drought among different drought indices.The results showed that precipitation and potential evapotranspiration have positive and major influences on vegetation in the nine major river basins at T1–T6 time scales.Soil moisture shows a lower degree of negative influence on vegetation in different river basins at multiple time scales.Potential evapotranspiration shows a higher degree of positive influence on vegetation,and it acts as the primary influencing factor with higher area proportion at multiple time scales in different river basins.The VDCI has a stronger relationship with the NDVI in the Songhua River and Liaohe River Basin,Haihe River Basin,Yellow River Basin,Huaihe River Basin and Yangtze River Basin at T1–T4 time scales.In general,the VDCI is more sensitive(with shorter lag time of vegetation response to drought)than the traditional drought indices(SPEI,scPDSI and SSMI)in monitoring vegetation drought,and thus it could be applied to monitor short-term vegetation drought.The VDCI developed in the study can reveal the law of unclear mechanisms between vegetation and climate,and can be applied in other fields of vegetation drought monitoring with complex mechanisms.
基金Under the auspices of the Second Tibetan Plateau Scientific Expedition and Research(STEP)Program(No.2019QZKK0103)National Natural Science Foundation of China(No.41772173,41405077)+1 种基金the Thousand Talents Program for High-end Innovation of Qinghai Provincethe Applied Basic Research Project of Qinghai Province(No.2019-zj-7045)。
文摘Variations in vegetation are closely related to climate change, but understanding of their characteristics and causes remains limited. As a typical semi-humid and semi-arid cold plateau region, it is important to understand the knowledge of long term Normalized Difference Vegetation Index(NDVI) variations and find the potential causes in the source region of the Yangtze River. Based on four tree-ring width chronologies, the regional mean NDVI for July and August spanning the period 1665–2013 was reconstructed using a regression model, and it explained 43.9% of the total variance during the period 1981–2013. In decadal, the reconstructed NDVI showed eight growth stages(1754–1764, 1766–1783, 1794–1811, 1828–1838, 1843–1855, 1862–1873, 1897–1909, and 1932–1945)and four degradation stages(1679–1698, 1726–1753, 1910–1923, and 1988–2000). And based on wavelet analysis, significant cycles of2–3 yr and 3–8 yr were identified. In additional, there was a significant positive correlation between the NDVI and the Palmer Drought Severity Index(PDSI) during the past 349 yr, and they were mainly in phase. However, according to the results of correlation analysis between different grades of drought/wet and NDVI, there was significant asymmetry in extreme drought years and extreme wet years. In extreme drought years, NDVI was positively correlated with PDSI, and in extreme wet years they were negatively correlated.
基金support for this research of Chinese Postdoctoral Science Foundation (2016T90961, 2015M570864)Openended fund of State Key Laboratory of Cryosphere Sciences, Chinese Academy of Sciences (SKLCSOP-2014-11)+2 种基金Project of Northwest Normal University (China) Young Teachers Scientific Research Ability Promotion Plan (NWNU-LKQN13-10)Project of National Natural Science Foundation of China (41271133, 41273010, 41361106, 41261104)Project of Major National Research Projects of China (No. 2013CBA01808)
文摘This study describes the spatial and temporal variation of a drought index and makes inferences regarding the environmental factors that influence this variability in the Hengduan Mountains. A drought index is typically used to determine the moisture conditions and the magnitude of water deficiency in a given area. Based on data from 26 meteorological stations over the period 1960-2012, the spatial and temporal variations of the drought index were analyzed using a thin plate smoothing splines method that considered elevation as a covariate. The drought index was estimated based on the potential evapotranspiration(E0) as defined by the Penman Monteith model modified by FAO(1998). The results of the reported analysis showed that the drought index in the Hengduan Mountains has been decreasing since 1960 at a rate of-0.008/a. This represented a progressive shift from the "sub-humid" class, which typified the wider area in the Hengduan Mountains, toward the "humid" class, which appeared in the Hengduan Mountains areas. The drought index was relatively high in the north and low in the south and the variation of the drought index varied with seasons. The drought index showed increasing trends in summer and autumn and it is greater in autumn than in summer, while it showed a decreasing trend in spring and winter. Drought index is inversely proportional to the soil relative humidity and Normalized Difference Vegetation Index(NDVI).
基金This work was supported by the Key Program of the National Natural Science Foundation o f China （Grant No. 41430861） and the Open Fund of Key Laboratory of Poyang Lake Wetland and Watershed Research, Ministry of Education, Jiangxi Normal University （PK2014010）. We thank the U.S. Geological Survey （USGS） and the Center for Earth Observation and Digital Earth （CEODE） for providing Landsat TM/ETM＋ data, and the Meteorological Information Center of China Meteorological Administration for providing agro-meteorological datasets. The critical comments of Professor Fang Hongliang from the Institute of Geographic Sciences and Natural Resources Research, and Senior Researcher Leon Braat from Wageningen University, helped to improve this manuscript. Thanks also go to Ms. Sarah Xiao from Yale University for her thoughtful English editing. We thank the anonymous reviewers for their insightful comments on earlier versions of the manuscript.
文摘Mapping rice cropping systems with optical imagery in multiple cropping regions is challenging due to cloud contamination and data availability; development of a phenology-based algorithm with a reduced data demand is essential. In this study, the Landsat-derived Renorma- lized Index of Normalized Difference Vegetation Index （RNDVI） was proposed based on two temporal windows in which the NDVI values of single and early （or late） rice display inverse changes, and then applied to discriminate rice cropping systems. The Poyang Lake Region （PLR）, characterized by a typical cropping system of single cropping rice （SCR, or single rice） and double cropping rice （DCR, including early rice and late rice）, was selected as a testing area. The results showed that NDVI data derived from Landsat time-series at eight to sixteen days captures the temporal development of paddy rice. There are two key phenological stages during the overlapping growth period in which the NDVI values of SCR and DCR change inversely, namely the ripening phase of early rice and the growing phase of single rice as well as the ripening stage of single rice and the growing stage of late rice. NDVI derived from scenes in two temporal windows, specifically early August and early October, was used to construct the RNDVI for discriminating rice cropping systems in the polder area of the PLR, China. Comparison with ground truth data indicates high classification accuracy. The RNDVI approach highlights the inverse variations of NDVI values due to the difference of rice growth between two temporal windows. This makes the discrimination of rice cropping systems straightforward as it only needs to distinguish whether the candidate rice typeis in the period of growth （RNDVI 〈 0） or senescence （RNDVI 〉 0）.
文摘Soil carbon stock research has gained prominence in environmental studies amidst climate change concerns,especially given that soil is one of the largest terrestrial carbon reserves.Accurate predictions necessitate comprehensive soil profile measurements,which are resource-intensive to obtain.To address this,depth functions are employed to derive continuous estimates,aligning with standardized depths.However,global datasets employing depth functions in raster format have not been widely utilized,which could lower financial costs and improve accuracy in data-scarce regions.Furthermore,research into aggregating depth functions for realistic carbon stock estimations remains limited,offering opportunities to streamline cost and time.The aim of this study was to apply equal-area splines to estimate soil carbon stocks,utilizing SoilGrids and iSDAsoil datasets in a 317-km^(2) Quaternary catchment(30°48′E,29°18′S)in KwaZulu-Natal,South Africa.Both datasets were resampled to a 250-m resolution,and the splines were interpolated to a depth of 50 cm per pixel.Various aggregation methods were employed in calculation,including the cumulative sum(definite integral),discrete sum(sum of 1-cm spline predictions),and the mean carbon stock(mean to 50 cm).Quantitative evaluation was performed with 310 external soil samples.SoilGrids showed higher predictions(100–546 kg m^(-2))than iSDAsoil(66.9–225 kg m^(-2))for the cumulative sum.The discrete sum also exhibited higher prediction values for SoilGrids(293–789 kg m^(-2))compared to iSDAsoil(228–557 kg m^(-2)).SoilGrids aggregated with the discrete sum closely matched previous studies,estimating total carbon stock for the catchment at 7126 t,albeit with spatial inconsistencies.However,when evaluating with an external dataset,the results were not satisfactory for any method according to Lin's concordance correlation coefficient(CCC,correlation of a 1:1 line),with all models obtaining a CCC below 0.01.Similarly,all models had a root mean squared error larger than 59 kg m^(-2).It was concluded that SoilGrids and iSDAsoil were spatially inaccurate in the catchment but can still provide information about the total carbon stock.This method could be improved by obtaining more soil samples for the datasets,incorporating local data into the spline,making the method more computationally efficient,and accounting for discrete horizon boundaries.
基金Under the auspices of the National Natural Science Foundation of China(No.32371863)Fundamental Research Funds for the Central Universities(No.2572025AW39)。
文摘Understanding the complex relationship between vegetation change and both natural and anthropogenic factors is a subject of global importance.However,comprehensive explanations of vegetation cover trends across China’s different regions and the dynamic roles of their drivers remain limited.This study analyzed national and regional vegetation change trends from 2000 to 2020 and evaluated the evolving impacts of natural and anthropogenic factors.Results indicate that 44.14%of China’s land experienced significant increase(P<0.05)in vegetation coverage.The Northeast(A1),Southwest(A5),and South China(A8)regions showed extremely significant increases in vegetation cover,with over 65%of vegetation exhibiting extremely significant growth(P<0.01).In contrast,less than 25%of vegetation in Inner Mongolia(A2),Northwest(A3),and the Qinghai-Tibetan Plateau(A4)subregions demonstrated an extremely significant increasing trend(P<0.01).Precipitation(q=0.766)and land use type(q=0.636)were the most influential natural and anthropogenic factors,respectively,with their interaction(q=0.838)dominating national vegetation patterns.On the west side of the Hu Line,vegetation dynamics were mainly limited by arid and semi-arid climates,with precipitation as the dominant factor,though land use measures have contributed to some vegetation improvement.Between 2000 and 2020,the influence of precipitation on vegetation cover increased in regions A3 and A4,with q-values rising by 26.73%and 101.13%,respectively.Additionally,soil type exerted a significant effect(P<0.001)on vegetation cover across all regions,being most pronounced in A2(q=0.692).On the east side of the Hu Line,vegetation growth benefited generally from warm and humid conditions,while local decline in urbanized areas was largely attributable to land use change and economic expansion.Concurrently anthropogenic factors such as land use and population density increasingly influenced vegetation dynamics in eastern urban areas of the Hu Line.Population density and GDP were the most influential factors affecting vegetation cover in region A8,with q-values of 0.443 and 0.380,respectively(P<0.001).Future efforts should maintain the benefits of large-scale ecological projects and harmonize the relationship between urban vegetation and anthropogenic influences.
基金supported by the Xinjiang Uygur Autonomous Region Major Scientific and Technological Special Project Research and Demonstration on the Development Model of Ecological Agriculture and Efficient Utilization of Soil and Water Resources in Modern Irrigation Areas(2023A02002-1).
文摘Climate change in High Mountain Asia(HMA)is characterized by elevation dependence,which results in vertical zoning of vegetation distribution.However,few studies have been conducted on the distribution patterns of vegetation,the response of vegetation to climate change,and the key climatic control factors of vegetation along the elevation gradient in this region.In this study,based on the Normalized Difference Vegetation index(NDVI),we investigated the evolution pattern of vegetation in HMA during 2001-2020 using linear trend and Bayesian Estimator of Abrupt change,Seasonality,and Trend(BEAST)methods.Pearson correlation analysis and partial correlation analysis were used to explore the response relationship between vegetation and climatic factors along the elevation gradient.Path analysis was employed to quantitatively reveal the dominant climatic factors affecting vegetation distribution along the elevation gradient.The results showed that NDVI in HMA increased at a rate of 0.011/10a from 2001 to 2020,and the rate of increase abruptly slowed down after 2017.NDVI showed a fluctuating increase at elevation zones 1-2(<2500 m)and then decreased at elevation zones 3-9(2500-6000 m)with the increase of elevation.NDVI was most sensitive to precipitation and temperature at a 1-month lag.With the increase of elevation,the positive response relationship of NDVI with precipitation gradually weakened,while that of NDVI with temperature was the opposite.The total effect coefficient of precipitation(0.95)on vegetation was larger than that of temperature(0.87),indicating that precipitation is the dominant control factor affecting vegetation growth.Spacially,vegetation growth is jointly influenced by precipitation and temperature,but the influence of precipitation on vegetation growth is dominant at each elevation zone.The results of this study contribute to understanding how the elevation gradient effect influences the response of vegetation to climate change in alpine ecosystems.
基金funded by the Science and Innovation Ministry of Spain(projects AGL 2012-33465,AGL 2016-76463-P,PID 2021-123675OB-C43,and TED 2021-129770B-C21)support by Margarita Salas postdoctoral fellowship(reference RCMS-22-G1T6IW-17-NLHJ80)of the Universidad Politécnica de Madrid
文摘Droughts impact forests by influencing various processes such as canopy greenness,tree growth,and reproduction,but most studies have only examined a few of these processes.More comprehensive assessments of forest responses to climate variability and water shortages are needed to improve forecasts of post-drought dynamics.Iberian forests are well-suited for evaluating these effects because they experience diverse climatic conditions and are dominated by various conifer and broadleaf species,many of which exhibit masting.We assessed how greenness,evaluated using the normalized difference vegetation index(NDVI),tree radial growth,and seed or cone production responded to drought in five tree species(three conifers:silver fir(Abies alba),Scots pine(Pinus sylvestris),and stone pine(Pinus pinea);two broadleaves:European beech(Fagus sylvatica)and holm oak(Quercus ilex)inhabiting sites with different aridity.We correlated these data with the standardized precipitation evapotranspiration index(SPEI)using the climate window analysis(climwin)package,which identifies the most relevant climate window.Drought constrained growth more than greenness and seed or cone production.Dry conditions led to high seed or cone production in species found in cool,moist sites(silver fir,beech,and Scots pine).We also found negative associations of cone production with summer SPEI in the drought-tolerant stone pine,which showed lagged growth-cone negative correlations.However,in the seasonally dry holm oak forests,severe droughts constrained both growth and acorn production,leading to a positive correlation between these variables.Drought impacts on greenness,growth,seed,and cone production depended on species phenology and site aridity.A negative correlation between growth and reproduction does not necessarily indicate trade-offs,as both may be influenced by similar climatic factors.
基金funded by the Shaanxi Provincial Department of Science and Technology(2023JCYB449)the Yan'an University Project(YDBK2017-19)+1 种基金the Yan'an Science and Technology Bureau's List System Project(2023SLJBZ002)the Shaanxi Provincial Department of Education Natural Science Special Project(23JK0725,24JK0716).
文摘Investigating the spatiotemporal evolution of vegetation and its response mechanisms to natural and anthropogenic elements is crucial for regional vegetation restoration and ecological preservation.The Mu Us Sandy Land(MUSL),which is situated in the semi-arid zone of northwestern China adjacent to the Loess Plateau,has been at the forefront of desertification and oasis formation over the past two millennia.This study is based on the synthesis of the Normalized Difference Vegetation Index(NDVI)data from MOD13A3 data in the MODIS(Moderate-Resolution Imaging Spectroradiometer)dataset(2002-2021)and climate data(temperature and precipitation)at annual and monthly scales from the National Earth System Science Data Center.A range of analytical methods,including univariate linear regression,Theil-Sen trend analysis and Mann-Kendall significance test,correlation analysis,residual analysis,and Hurst index,were used to explore the response mechanisms of the NDVI to climate change and human activities and to predict the future trends of the NDVI in the MUSL.The results showed that through the method of correlation analysis,in terms of both spatially averaged correlation coefficients and area proportion,the NDVI was positively correlated with temperature and precipitation in 97.59%and 96.51%of the study area,respectively,indicating that temperature has a greater impact on the NDVI than precipitation.Residual analysis quantified the contributions of climate change and human activities to the NDVI changes,revealing that climate change and human activities contribute up to 30.00%and 70.00%,respectively,suggesting that human activities predominantly affect the NDVI changes in the MUSL.The Hurst index was used to categorize the future trend of the NDVI into four main directions of development:continuous degradation(0.05%of the study area),degradation in the past but improvement in the future(54.45%),improvement in the past but degradation in the future(0.13%),and continuous improvement(45.36%).In more than 50.00%of the regions that have been degraded in the past but were expected to improve in the future,the NDVI was expected to exhibit a stable trend of anti-persistent improvement.These findings provide theoretical support for future ecological protection,planning,and the implementation of ecological engineering in the MUSL,and also offer a theoretical basis for the planning and execution of construction projects,environmental protection measures,and the sustainable development of vegetation.
基金funded by the National Natural Science Foundation of China(42361017)the Gansu Provincial Science and Technology Program-Special Program for Key Research and Development(R&D)on Ecological Civilization Construction in Gansu Province(24YFFA050)the Gansu Agricultural University-Gansu Provincial Academy of Natural Resources Planning Joint Graduate Training Base Project(GAU2024-003)。
文摘The Loess Plateau(LP),one of the most ecologically fragile regions in China,is affected by severe soil erosion and environmental degradation.Despite large-scale ecological restoration efforts made by Chinese government in recent years,the region continues to face significant ecological challenges due to the combined impact of climate change and human activities.In this context,we developed a kernal Remote Sensing Ecological Index(kRSEI)using Moderate Resolution Imaging Spectroradiometer(MODIS)products on the Google Earth Engine(GEE)platform to analyze the spatiotemporal patterns and trends in ecological environmental quality(EEQ)across the LP from 2000 to 2022 and project future trajectories.Then,we applied partial correlation analysis and multivariate regression residual analysis to further quantify the relative contributions of climate change and human activities to EEQ.During the study period,the kRSEI values exhibited significant spatial heterogeneity,with a stepwise degradation pattern in the southeast to northwest across the LP.The maximum(0.51)and minimum(0.46)values of the kRSEI were observed in 2007 and 2021,respectively.Trend analyses revealed a decline in EEQ across the LP.Hurst exponent analysis predicted a trend of weak anti-persistent development in most of the plateau areas in the future.A positive correlation was identified between kRSEI and precipitation,particularly in the central and western regions;although,improvements were limited by a precipitation threshold of 837.66 mm/a.A moderate increase in temperature was shown to potentially benefit the ecological environment within a certain range;however,temperature of-1.00°C-7.95°C often had a negative impact on the ecosystem.Climate change and human activities jointly influenced 65.78%of LP area on EEQ,primarily having a negative impact.In terms of contribution,human activities played a dominant role in driving changes in EEQ across the plateau.These findings provide crucial insights for accurately assessing the ecological state of the LP and suggest the design of future restoration strategies.
基金supported by the National Natural Science Foundation of China(No.42061065)the Third Xinjiang Comprehensive Scientific Expedition,China(No.2022xjkk03010102).
文摘Root zone soil moisture(RZSM)plays a critical role in land-atmosphere hydrological cycles and serves as the primary water source for vegetation growth.However,the correlations between RZSM and its associated variables,including surface soil moisture(SSM),often exhibit nonlinearities that are challenging to identify and quantify using conventional statistical techniques.Therefore,this study presents a hybrid convolutional neural network(CNN)-long short-term memory neural network(LSTM)-attention(CLA)model for predicting RZSM.Owing to the scarcity of soil moisture(SM)observation data,the physical model Hydrus-1D was employed to simulate a comprehensive dataset of spatial-temporal SM.Meteorological data and moderate resolution imaging spectroradiometer vegetation characterization parameters were used as predictor variables for the training and validation of the CLA model.The results of the CLA model for SM prediction in the root zone were significantly enhanced compared with those of the traditional LSTM and CNN-LSTM models.This was particularly notable at the depth of 80–100 cm,where the fitness(R^(2))reached nearly 0.9298.Moreover,the root mean square error of the CLA model was reduced by 49%and 57%compared with those of the LSTM and CNN-LSTM models,respectively.This study demonstrates that the integration of physical modeling and deep learning methods provides a more comprehensive and accurate understanding of spatial-temporal SM variations in the root zone.
文摘As global urbanization accelerates,with the urban population projected to reach 68%by 2050,the impact of urban environments on eye health has emerged as a significant public health issue.From the"global pandemic"of myopia to the surge in diabetic retinopathy,from visual fatigue induced by blue light exposure to age-related glaucoma and macular degeneration,urban lifestyles are reshaping the epidemiological landscape of ophthalmic diseases.This issue focuses on"Urban Eye Health,"aiming to integrate cutting-edge research findings worldwide and explore how to establish a sustainable eye health management system through technological innovation,policy coordination,and community intervention.
文摘This research aims to analyse the spatio-temporal changes of vegetation cover in coastal regions of Char Fasson and Galachipa Upazila, Bangladesh for a period of 30 years (1994-2024) based on Landsat satellite imagery and NDVI. Through the evaluation of NDVI this paper classifies vegetation as no water/bare vegetation, slightly densed vegetation, moderately densed vegetation, and highly densed vegetation. The findings reveal significant fluctuations in vegetation cover: from 1994 to 2004, there has been an increase in vegetation density implying that afforestation has created more moderate and highly densed vegetation out of density vegetation. However, between 2004 and 2014, vegetation cover decreased because some cyclones, like Sidr and Aila, affected the coastal forest of Bangladesh. Other attempts to afforestation supported improved coverage from vegetation between 2014 and 2024. These findings provide clear evidence of the sustainable benefits of coastal afforestation in the reduction of coastal erosion and storm surges that affect vegetation and coasts. Knowledge gained in this research is highly useful to the environmental planners on recommendations for sustainable land uses and preservation to build up ecological stability in Bangladesh weak coastal areas.
