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
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
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
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
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
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
Climate change significantly affects vegetation dynamics.Thus,understanding interactions between vegetation and climatic factors is essential for ecological management.This study used kernel Normalized Difference Vege...Climate change significantly affects vegetation dynamics.Thus,understanding interactions between vegetation and climatic factors is essential for ecological management.This study used kernel Normalized Difference Vegetation Index(kNDVI)and climatic data(temperature,precipitation,humidity,and vapor pressure deficit(VPD))of China from 2000 to 2022,integrating Geographic Convergent Cross Mapping(GCCM)causal modeling,Extreme Gradient Boosting-Shapley Additive Explanations(XGBoost-SHAP)nonlinear threshold identification,and Geographical Simulation and Optimization Systems-Future Land Use Simulation(GeoSOS-FLUS)spatial prediction modeling to investigate vegetation spatiotemporal characteristics,driving mechanisms,nonlinear thresholds,and future spatial patterns.Results indicated that from 2000 to 2022,China's kNDVI showed an overall increasing trend(annual average ranging from 0.29 to 0.33 with distinct spatial differentiation:52.77%of areas locating in agricultural and ecological restoration regions in the central-eastern plain)experienced vegetation improvement,whereas 2.68%of areas locating in the southeastern coastal urbanized regions and the Yangtze River Delta experience vegetation degradation.The coefficient of variation(CV)of kNDVI at 0.30–0.40(accounting for 10.61%)was significantly higher than that of NDVI(accounting for 1.80%).Climate-driven mechanisms exhibited notable library length(L)dependence.At short-term scales(L<50),vegetation-driven transpiration regulated local microclimate,with a causal strength from kNDVI to temperature of 0.04–0.15;at long-term scales(L>100),cumulative temperature effects dominated vegetation dynamics,with a causal strength from temperature to kNDVI of 0.33.Humidity and kNDVI formed bidirectional positive feedback at long-term scales(L=210,causal strength>0.70),whereas the long-term suppressive effect of VPD was particularly pronounced(causal strength=0.21)in arid areas.The optimal threshold intervals identified were temperature at–12.18℃–0.67℃,precipitation at 24.00–159.74 mm,humidity of lower than 22.00%,and VPD of<0.07,0.17–0.24,and>0.30 kPa;notably,the lower precipitation threshold(24.00 mm)represented the minimum water requirements for vegetation recovery in arid areas.Future kNDVI spatial patterns are projected to continue the trend of"southeastern optimization and northwestern delay"from 2025 to 2040:the area proportion of high kNDVI value(>0.50)will rise from 40.43%to 41.85%,concentrated in the Sichuan Basin and the southern hills;meanwhile,the proportion of low-value areas of kNDVI(0.00–0.10)in the arid northwestern areas will decline by only 1.25%,constrained by sustained temperature and VPD stress.This study provides a scientific basis for vegetation dynamic regulation and sustainable development under climate change.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 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
The Three-River Source Region(TRSR)in China holds a vital position and exhibits an irreplaceable strategic importance in ecological preservation at the national level.On the basis of an in-depth study of the vegetatio...The Three-River Source Region(TRSR)in China holds a vital position and exhibits an irreplaceable strategic importance in ecological preservation at the national level.On the basis of an in-depth study of the vegetation evolution in the TRSR from 2000 to 2022,we conducted a detailed analysis of the feedback mechanism of vegetation growth to climate change and human activity for different vegetation types.During the growing season,the spatiotemporal variations of normalized difference vegetation index(NDVI)for different vegetation types in the TRSR were analyzed using the Moderate Resolution Imaging Spectroradiometer(MODIS)-NDVI data and meteorological data from 2000 to 2022.In addition,the response characteristics of vegetation to temperature,precipitation,and human activity were assessed using trend analysis,partial correlation analysis,and residual analysis.Results indicated that,after in-depth research,from 2000 to 2022,the TRSR's average NDVI during the growing season was 0.3482.The preliminary ranking of the average NDVI for different vegetation types was as follows:shrubland(0.5762)>forest(0.5443)>meadow(0.4219)>highland vegetation(0.2223)>steppe(0.2159).The NDVI during the growing season exhibited a fluctuating growth trend,with an average growth rate of 0.0018/10a(P<0.01).Notably,forests displayed a significant development trend throughout the growing season,possessing the fastest rate of change in NDVI(0.0028/10a).Moreover,the upward trends in NDVI for forests and steppes exhibited extensive spatial distributions,with significant increases accounting for 95.23%and 93.80%,respectively.The sensitivity to precipitation was significantly enhanced in other vegetation types other than highland vegetation.By contrast,steppes,meadows,and highland vegetation demonstrated relatively high vulnerability to temperature fluctuations.A further detailed analysis revealed that climate change had a significant positive impact on the TRSR from 2000 to 2022,particularly in its northwestern areas,accounting for 85.05%of the total area.Meanwhile,human activity played a notable positive role in the southwestern and southeastern areas of the TRSR,covering 62.65%of the total area.Therefore,climate change had a significantly higher impact on NDVI during the growing season in the TRSR than human activity.展开更多
Understanding the response of vegetation variation to climate change and human activities is critical for addressing future conflicts between humans and the environment,and maintaining ecosystem stability.Here,we aime...Understanding the response of vegetation variation to climate change and human activities is critical for addressing future conflicts between humans and the environment,and maintaining ecosystem stability.Here,we aimed to identify the determining factors of vegetation variation and explore the sensitivity of vegetation to temperature(SVT)and the sensitivity of vegetation to precipitation(SVP)in the Shiyang River Basin(SYRB)of China during 2001-2022.The climate data from climatic research unit(CRU),vegetation index data from Moderate Resolution Imaging Spectroradiometer(MODIS),and land use data from Landsat images were used to analyze the spatial-temporal changes in vegetation indices,climate,and land use in the SYRB and its sub-basins(i.e.,upstream,midstream,and downstream basins)during 2001-2022.Linear regression analysis and correlation analysis were used to explore the SVT and SVP,revealing the driving factors of vegetation variation.Significant increasing trends(P<0.05)were detected for the enhanced vegetation index(EVI)and normalized difference vegetation index(NDVI)in the SYRB during 2001-2022,with most regions(84%)experiencing significant variation in vegetation,and land use change was determined as the dominant factor of vegetation variation.Non-significant decreasing trends were detected in the SVT and SVP of the SYRB during 2001-2022.There were spatial differences in vegetation variation,SVT,and SVP.Although NDVI and EVI exhibited increasing trends in the upstream,midstream,and downstream basins,the change slope in the downstream basin was lower than those in the upstream and midstream basins,the SVT in the upstream basin was higher than those in the midstream and downstream basins,and the SVP in the downstream basin was lower than those in the upstream and midstream basins.Temperature and precipitation changes controlled vegetation variation in the upstream and midstream basins while human activities(land use change)dominated vegetation variation in the downstream basin.We concluded that there is a spatial heterogeneity in the response of vegetation variation to climate change and human activities across different sub-basins of the SYRB.These findings can enhance our understanding of the relationship among vegetation variation,climate change,and human activities,and provide a reference for addressing future conflicts between humans and the environment in the arid inland river basins.展开更多
Changes in vegetation status generally also represents changes in the ecological health of islands and reefs(IRs).However,studies are limited of drivers and trends of vegetation change of Nansha Islands,China and how ...Changes in vegetation status generally also represents changes in the ecological health of islands and reefs(IRs).However,studies are limited of drivers and trends of vegetation change of Nansha Islands,China and how they relate to climate change and human activities.To resolve this limitation,we studied changes to the Normalized Difference Vegetation Index(NDVI)vegetation-greenness index for 22 IRs of Nansha Islands during normal and extreme conditions.Trends of vegetation greenness were analyzed using Sen's slope and Mann-Kendall test at two spatial scales(pixel and island),and driving factor analyses were performed by time-lagged partial correlation analyses.These were related to impacts from human activities and climatic factors under normal(temperature,precipitation,radiation,and Normalized Difference Built-up Index(NDBI))and extreme conditions(wind speed and latitude of IRs)from 2016 to 2022.Results showed:1)among the 22 IRs,NDVI increased/decreased significantly in 15/4 IRs,respectively.Huayang Reef had the highest NDVI change-rate(0.48%/mon),and Zhongye Island had the lowest(–0.29%/mon).Local spatial patterns were in one of two forms:dotted-form,and degradation in banded-form.2)Under normal conditions,human activities(characterized by NDBI)had higher impacts on vegetation-greenness than other factors.3)Under extreme conditions,wind speed(R^(2)=0.2337,P<0.05)and latitude(R^(2)=0.2769,P<0.05)provided limited explanation for changes from typhoon events.Our results provide scientific support for the sustainable development of Nansha Islands and the United Nations‘Ocean Decade’initiative.展开更多
The Mongolian Plateau in East Asia is one of the largest contingent arid and semi-arid areas of the world.Under the impacts of climate change and human activities,desertification is becoming increasingly severe on the...The Mongolian Plateau in East Asia is one of the largest contingent arid and semi-arid areas of the world.Under the impacts of climate change and human activities,desertification is becoming increasingly severe on the Mongolian Plateau.Understanding the vegetation dynamics in this region can better characterize its ecological changes.In this study,based on Moderate Resolution Imaging Spectroradiometer(MODIS)images,we calculated the kernel normalized difference vegetation index(kNDVI)on the Mongolian Plateau from 2000 to 2023,and analyzed the changes in kNDVI using the Theil-Sen median trend analysis and Mann-Kendall significance test.We further investigated the impact of climate change on kNDVI change using partial correlation analysis and composite correlation analysis,and quantified the effects of climate change and human activities on kNDVI change by residual analysis.The results showed that kNDVI on the Mongolian Plateau was increasing overall,and the vegetation recovery area in the southern region was significantly larger than that in the northern region.About 50.99%of the plateau showed dominant climate-driven effects of temperature,precipitation,and wind speed on kNDVI change.Residual analysis showed that climate change and human activities together contributed to 94.79%of the areas with vegetation improvement.Appropriate human activities promoted the recovery of local vegetation,and climate change inhibited vegetation growth in the northern part of the Mongolian Plateau.This study provides scientific data for understanding the regional ecological environment status and future changes and developing effective ecological protection measures on the Mongolian Plateau.展开更多
Permafrost in Northeast China is undergoing extensive and rapid degradation,and it is of great importance to understand the dynamics of vegetation response to permafrost degradation during different periods in this re...Permafrost in Northeast China is undergoing extensive and rapid degradation,and it is of great importance to understand the dynamics of vegetation response to permafrost degradation during different periods in this region.Based on the meteorological station data and MODIS land surface temperature data,we mapped the distribution of permafrost using the surface frost number(SFN)model to analyze the permafrost degradation processes in Northeast China from 1981 to 2020.We investigated the spatiotemporal variation characteristics of vegetation and its response to permafrost degradation during different periods from 1982 to 2020 using the normalized difference vegetation index(NDVI).We further discussed the dominant factors influencing the vegetation dynamics in the permafrost degradation processes.Results indicated that the permafrost area in Northeast China decreased significantly by 1.01×10^(5) km^(2) in the past 40 a.The permafrost stability continued to weaken,with large areas of stable permafrost(SP)converted to semi-stable permafrost(SSP)and unstable permafrost(UP)after 2000.From 1982 to 2020,NDVI exhibited a significant decreasing trend in the seasonal frost(SF)region,while it exhibited an increasing trend in the permafrost region.NDVI in the UP and SSP regions changed from a significant increasing trend before 2000 to a nonsignificant decreasing trend after 2000.In 78.63%of the permafrost region,there was a negative correlation between the SFN and NDVI from 1982 to 2020.In the SP and SSP regions,the correlation between the SFN and NDVI was predominantly negative,while in the UP region,it was predominantly positive.Temperature was the dominant factor influencing the NDVI variations in the permafrost region from 1982 to 2020,and the impact of precipitation on NDVI variations increased after 2000.The findings elucidate the complex dynamics of vegetation in the permafrost region of Northeast China and provide deeper insights into the response mechanisms of vegetation in cold regions to permafrost degradation induced by climate change.展开更多
Little is known about the mechanism of climate-vegetation coverage coupled changes in the Tibetan Plateau(TP)region,which is the most climatically sensitive and ecologically fragile region with the highest terrain in ...Little is known about the mechanism of climate-vegetation coverage coupled changes in the Tibetan Plateau(TP)region,which is the most climatically sensitive and ecologically fragile region with the highest terrain in the world.This study,using multisource datasets(including satellite data and meteorological observations and reanalysis data)revealed the mutual feedback mechanisms between changes in climate(temperature and precipitation)and vegetation coverage in recent decades in the Hengduan Mountains Area(HMA)of the southeastern TP and their influences on climate in the downstream region,the Sichuan Basin(SCB).There is mutual facilitation between rising air temperature and increasing vegetation coverage in the HMA,which is most significant during winter,and then during spring,but insignificant during summer and autumn.Rising temperature significantly enhances local vegetation coverage,and vegetation greening in turn heats the atmosphere via enhancing net heat flux from the surface to the atmosphere.The atmospheric heating anomaly over the HMA thickens the atmospheric column and increases upper air pressure.The high pressure anomaly disperses downstream via the westerly flow,expands across the SCB,and eventually increases the SCB temperature.This effect lasts from winter to the following spring,which may cause the maximum increasing trend of the SCB temperature and vegetation coverage in spring.These results are helpful for estimating future trends in climate and eco-environmental variations in the HMA and SCB under warming scenarios,as well as seasonal forecasting based on the connection between the HMA eco-environment and SCB climate.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
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
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
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 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 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.
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.
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.
基金funded by the Key Science and Technology Research Projects of Henan Province(252102320172).
文摘Climate change significantly affects vegetation dynamics.Thus,understanding interactions between vegetation and climatic factors is essential for ecological management.This study used kernel Normalized Difference Vegetation Index(kNDVI)and climatic data(temperature,precipitation,humidity,and vapor pressure deficit(VPD))of China from 2000 to 2022,integrating Geographic Convergent Cross Mapping(GCCM)causal modeling,Extreme Gradient Boosting-Shapley Additive Explanations(XGBoost-SHAP)nonlinear threshold identification,and Geographical Simulation and Optimization Systems-Future Land Use Simulation(GeoSOS-FLUS)spatial prediction modeling to investigate vegetation spatiotemporal characteristics,driving mechanisms,nonlinear thresholds,and future spatial patterns.Results indicated that from 2000 to 2022,China's kNDVI showed an overall increasing trend(annual average ranging from 0.29 to 0.33 with distinct spatial differentiation:52.77%of areas locating in agricultural and ecological restoration regions in the central-eastern plain)experienced vegetation improvement,whereas 2.68%of areas locating in the southeastern coastal urbanized regions and the Yangtze River Delta experience vegetation degradation.The coefficient of variation(CV)of kNDVI at 0.30–0.40(accounting for 10.61%)was significantly higher than that of NDVI(accounting for 1.80%).Climate-driven mechanisms exhibited notable library length(L)dependence.At short-term scales(L<50),vegetation-driven transpiration regulated local microclimate,with a causal strength from kNDVI to temperature of 0.04–0.15;at long-term scales(L>100),cumulative temperature effects dominated vegetation dynamics,with a causal strength from temperature to kNDVI of 0.33.Humidity and kNDVI formed bidirectional positive feedback at long-term scales(L=210,causal strength>0.70),whereas the long-term suppressive effect of VPD was particularly pronounced(causal strength=0.21)in arid areas.The optimal threshold intervals identified were temperature at–12.18℃–0.67℃,precipitation at 24.00–159.74 mm,humidity of lower than 22.00%,and VPD of<0.07,0.17–0.24,and>0.30 kPa;notably,the lower precipitation threshold(24.00 mm)represented the minimum water requirements for vegetation recovery in arid areas.Future kNDVI spatial patterns are projected to continue the trend of"southeastern optimization and northwestern delay"from 2025 to 2040:the area proportion of high kNDVI value(>0.50)will rise from 40.43%to 41.85%,concentrated in the Sichuan Basin and the southern hills;meanwhile,the proportion of low-value areas of kNDVI(0.00–0.10)in the arid northwestern areas will decline by only 1.25%,constrained by sustained temperature and VPD stress.This study provides a scientific basis for vegetation dynamic regulation and sustainable development under climate change.
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supported by the National Natural Science Foundation of China (42377472, 42174055)the Jiangxi Provincial Social Science "Fourteenth Five-Year Plan" (2024) Fund Project (24GL45)+1 种基金the Research Center of Resource and Environment Economics (20RGL01)the Provincial Finance Project of Jiangxi Academy of Sciences-Young Talent Cultivation Program (2023YSBG50010)
文摘The Three-River Source Region(TRSR)in China holds a vital position and exhibits an irreplaceable strategic importance in ecological preservation at the national level.On the basis of an in-depth study of the vegetation evolution in the TRSR from 2000 to 2022,we conducted a detailed analysis of the feedback mechanism of vegetation growth to climate change and human activity for different vegetation types.During the growing season,the spatiotemporal variations of normalized difference vegetation index(NDVI)for different vegetation types in the TRSR were analyzed using the Moderate Resolution Imaging Spectroradiometer(MODIS)-NDVI data and meteorological data from 2000 to 2022.In addition,the response characteristics of vegetation to temperature,precipitation,and human activity were assessed using trend analysis,partial correlation analysis,and residual analysis.Results indicated that,after in-depth research,from 2000 to 2022,the TRSR's average NDVI during the growing season was 0.3482.The preliminary ranking of the average NDVI for different vegetation types was as follows:shrubland(0.5762)>forest(0.5443)>meadow(0.4219)>highland vegetation(0.2223)>steppe(0.2159).The NDVI during the growing season exhibited a fluctuating growth trend,with an average growth rate of 0.0018/10a(P<0.01).Notably,forests displayed a significant development trend throughout the growing season,possessing the fastest rate of change in NDVI(0.0028/10a).Moreover,the upward trends in NDVI for forests and steppes exhibited extensive spatial distributions,with significant increases accounting for 95.23%and 93.80%,respectively.The sensitivity to precipitation was significantly enhanced in other vegetation types other than highland vegetation.By contrast,steppes,meadows,and highland vegetation demonstrated relatively high vulnerability to temperature fluctuations.A further detailed analysis revealed that climate change had a significant positive impact on the TRSR from 2000 to 2022,particularly in its northwestern areas,accounting for 85.05%of the total area.Meanwhile,human activity played a notable positive role in the southwestern and southeastern areas of the TRSR,covering 62.65%of the total area.Therefore,climate change had a significantly higher impact on NDVI during the growing season in the TRSR than human activity.
基金National Natural Science Foundation of China(42230720).
文摘Understanding the response of vegetation variation to climate change and human activities is critical for addressing future conflicts between humans and the environment,and maintaining ecosystem stability.Here,we aimed to identify the determining factors of vegetation variation and explore the sensitivity of vegetation to temperature(SVT)and the sensitivity of vegetation to precipitation(SVP)in the Shiyang River Basin(SYRB)of China during 2001-2022.The climate data from climatic research unit(CRU),vegetation index data from Moderate Resolution Imaging Spectroradiometer(MODIS),and land use data from Landsat images were used to analyze the spatial-temporal changes in vegetation indices,climate,and land use in the SYRB and its sub-basins(i.e.,upstream,midstream,and downstream basins)during 2001-2022.Linear regression analysis and correlation analysis were used to explore the SVT and SVP,revealing the driving factors of vegetation variation.Significant increasing trends(P<0.05)were detected for the enhanced vegetation index(EVI)and normalized difference vegetation index(NDVI)in the SYRB during 2001-2022,with most regions(84%)experiencing significant variation in vegetation,and land use change was determined as the dominant factor of vegetation variation.Non-significant decreasing trends were detected in the SVT and SVP of the SYRB during 2001-2022.There were spatial differences in vegetation variation,SVT,and SVP.Although NDVI and EVI exhibited increasing trends in the upstream,midstream,and downstream basins,the change slope in the downstream basin was lower than those in the upstream and midstream basins,the SVT in the upstream basin was higher than those in the midstream and downstream basins,and the SVP in the downstream basin was lower than those in the upstream and midstream basins.Temperature and precipitation changes controlled vegetation variation in the upstream and midstream basins while human activities(land use change)dominated vegetation variation in the downstream basin.We concluded that there is a spatial heterogeneity in the response of vegetation variation to climate change and human activities across different sub-basins of the SYRB.These findings can enhance our understanding of the relationship among vegetation variation,climate change,and human activities,and provide a reference for addressing future conflicts between humans and the environment in the arid inland river basins.
基金Under the auspices of National Key Research and Development Program of China (No.2022YFC3103103)。
文摘Changes in vegetation status generally also represents changes in the ecological health of islands and reefs(IRs).However,studies are limited of drivers and trends of vegetation change of Nansha Islands,China and how they relate to climate change and human activities.To resolve this limitation,we studied changes to the Normalized Difference Vegetation Index(NDVI)vegetation-greenness index for 22 IRs of Nansha Islands during normal and extreme conditions.Trends of vegetation greenness were analyzed using Sen's slope and Mann-Kendall test at two spatial scales(pixel and island),and driving factor analyses were performed by time-lagged partial correlation analyses.These were related to impacts from human activities and climatic factors under normal(temperature,precipitation,radiation,and Normalized Difference Built-up Index(NDBI))and extreme conditions(wind speed and latitude of IRs)from 2016 to 2022.Results showed:1)among the 22 IRs,NDVI increased/decreased significantly in 15/4 IRs,respectively.Huayang Reef had the highest NDVI change-rate(0.48%/mon),and Zhongye Island had the lowest(–0.29%/mon).Local spatial patterns were in one of two forms:dotted-form,and degradation in banded-form.2)Under normal conditions,human activities(characterized by NDBI)had higher impacts on vegetation-greenness than other factors.3)Under extreme conditions,wind speed(R^(2)=0.2337,P<0.05)and latitude(R^(2)=0.2769,P<0.05)provided limited explanation for changes from typhoon events.Our results provide scientific support for the sustainable development of Nansha Islands and the United Nations‘Ocean Decade’initiative.
基金National Key Research and Development Program on Enhancement of Soil and Water Ecological Security and Guarantee Technology in Desert Oasis Areas(2023YFF130420103)Three North Project of Xinhua Forestry Highland Demonstration Science and Technology Construction Project,the Technology and Demonstration of Near-Natural Modification of Artificial Protective Forest Structures and Enhancement of Soil and Water Conservation Functions in Ecological Protection Belt(2023YFF1305201)+2 种基金Multi-dimensional Coupled Soil-surface-groundwater Hydrological Processes and Vegetation Regulation Mechanism in Loess Area of the National Natural Science Foundation of China(U2243202)Hot Tracking Program of Beijing Forestry University"Planting a Billion Trees"Program and China-Mongolia Cooperation on Desertification in China(2023BLRD04)Research on Ecological Photovoltaic Vegetation Configuration Model and Restoration Technology(AMKJ2023-17).
文摘The Mongolian Plateau in East Asia is one of the largest contingent arid and semi-arid areas of the world.Under the impacts of climate change and human activities,desertification is becoming increasingly severe on the Mongolian Plateau.Understanding the vegetation dynamics in this region can better characterize its ecological changes.In this study,based on Moderate Resolution Imaging Spectroradiometer(MODIS)images,we calculated the kernel normalized difference vegetation index(kNDVI)on the Mongolian Plateau from 2000 to 2023,and analyzed the changes in kNDVI using the Theil-Sen median trend analysis and Mann-Kendall significance test.We further investigated the impact of climate change on kNDVI change using partial correlation analysis and composite correlation analysis,and quantified the effects of climate change and human activities on kNDVI change by residual analysis.The results showed that kNDVI on the Mongolian Plateau was increasing overall,and the vegetation recovery area in the southern region was significantly larger than that in the northern region.About 50.99%of the plateau showed dominant climate-driven effects of temperature,precipitation,and wind speed on kNDVI change.Residual analysis showed that climate change and human activities together contributed to 94.79%of the areas with vegetation improvement.Appropriate human activities promoted the recovery of local vegetation,and climate change inhibited vegetation growth in the northern part of the Mongolian Plateau.This study provides scientific data for understanding the regional ecological environment status and future changes and developing effective ecological protection measures on the Mongolian Plateau.
基金funded by the National Natural Science Foundation of China(41641024)the Science and the Technology Project of Heilongjiang Communications Investment Group(JT-100000-ZC-FW-2021-0182)the Field Scientific Observation and Research Station of the Ministry of Education-Geological Environment System of the Permafrost Area in Northeast China(MEORS-PGSNEC).
文摘Permafrost in Northeast China is undergoing extensive and rapid degradation,and it is of great importance to understand the dynamics of vegetation response to permafrost degradation during different periods in this region.Based on the meteorological station data and MODIS land surface temperature data,we mapped the distribution of permafrost using the surface frost number(SFN)model to analyze the permafrost degradation processes in Northeast China from 1981 to 2020.We investigated the spatiotemporal variation characteristics of vegetation and its response to permafrost degradation during different periods from 1982 to 2020 using the normalized difference vegetation index(NDVI).We further discussed the dominant factors influencing the vegetation dynamics in the permafrost degradation processes.Results indicated that the permafrost area in Northeast China decreased significantly by 1.01×10^(5) km^(2) in the past 40 a.The permafrost stability continued to weaken,with large areas of stable permafrost(SP)converted to semi-stable permafrost(SSP)and unstable permafrost(UP)after 2000.From 1982 to 2020,NDVI exhibited a significant decreasing trend in the seasonal frost(SF)region,while it exhibited an increasing trend in the permafrost region.NDVI in the UP and SSP regions changed from a significant increasing trend before 2000 to a nonsignificant decreasing trend after 2000.In 78.63%of the permafrost region,there was a negative correlation between the SFN and NDVI from 1982 to 2020.In the SP and SSP regions,the correlation between the SFN and NDVI was predominantly negative,while in the UP region,it was predominantly positive.Temperature was the dominant factor influencing the NDVI variations in the permafrost region from 1982 to 2020,and the impact of precipitation on NDVI variations increased after 2000.The findings elucidate the complex dynamics of vegetation in the permafrost region of Northeast China and provide deeper insights into the response mechanisms of vegetation in cold regions to permafrost degradation induced by climate change.
基金the National Natural Science Foundation of China(Grant Nos.42205059 and 42005075)the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant Nos.XDA23090303 and XDB40010302)+1 种基金the State Key Laboratory of Cryospheric Science(Grant No.SKLCS-ZZ-2024 and SKLCS-ZZ-2023)the Key Laboratory of Mountain Hazards and Earth Surface Processes.
文摘Little is known about the mechanism of climate-vegetation coverage coupled changes in the Tibetan Plateau(TP)region,which is the most climatically sensitive and ecologically fragile region with the highest terrain in the world.This study,using multisource datasets(including satellite data and meteorological observations and reanalysis data)revealed the mutual feedback mechanisms between changes in climate(temperature and precipitation)and vegetation coverage in recent decades in the Hengduan Mountains Area(HMA)of the southeastern TP and their influences on climate in the downstream region,the Sichuan Basin(SCB).There is mutual facilitation between rising air temperature and increasing vegetation coverage in the HMA,which is most significant during winter,and then during spring,but insignificant during summer and autumn.Rising temperature significantly enhances local vegetation coverage,and vegetation greening in turn heats the atmosphere via enhancing net heat flux from the surface to the atmosphere.The atmospheric heating anomaly over the HMA thickens the atmospheric column and increases upper air pressure.The high pressure anomaly disperses downstream via the westerly flow,expands across the SCB,and eventually increases the SCB temperature.This effect lasts from winter to the following spring,which may cause the maximum increasing trend of the SCB temperature and vegetation coverage in spring.These results are helpful for estimating future trends in climate and eco-environmental variations in the HMA and SCB under warming scenarios,as well as seasonal forecasting based on the connection between the HMA eco-environment and SCB climate.
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.
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.
