Dynamic Drought Monitoring in Guangxi Using Revised Temperature Vegetation Dryness Index

Moderate resolution imaging spectroradiometer （MODIS） data are very suitable for vast extent, long term and dynamic drought monitoring for its high temporal resolution, high spectral resolution and moderate spatial resolution. The composite Enhanced Vegetation Index （EVI） and composite land surface temperature （Ts） obtained from MODIS data MOD11A2 and MOD13A2 were used to construct the EVI-Ts space. And Temperature Vegetation Dryness Index （TVDI） was calculated to evaluate the agriculture drought in Guangxi province, China in October of 2006. The results showed that the drought area in Guangxi was evidently increasing and continuously deteriorating from the middle of September to the middle of November. The TVDI, coming from the EVI-Ts space, could effectively indicate the spatial distribution and temporal evolution of drought, so that it could provide a strong technical support for the forecasting agricultural drought in south China.

Climate Change and Ecological Projects Jointly Promote Vegetation Restoration in Three-River Source Region of China

As the source of the Yellow River,Yangtze River,and Lancang River,the Three-River Source Region(TRSR)in China is very important to China’s ecological security.In recent decades,TRSR’s ecosystem has degraded because of climate change and human disturbances.Therefore,a range of ecological projects were initiated by Chinese government around 2000 to curb further degradation.Current research shows that the vegetation of the TRSR has been initially restored over the past two decades,but the respective contribution of ecological projects and climate change in vegetation restoration has not been clarified.Here,we used the Moderate Resolution Imaging Spectroradiometer(MODIS)Enhanced Vegetation Index(EVI)to assess the spatial-temporal variations in vegetation and explore the impact of climate and human actions on vegetation in TRSR during 2001–2018.The results showed that about 26.02%of the TRSR had a significant increase in EVI over the 18 yr,with an increasing rate of 0.010/10 yr(P<0.05),and EVI significantly decreased in only 3.23%of the TRSR.Residual trend analysis indicated vegetation restoration was jointly promoted by climate and human actions,and the promotion of human actions was greater compared with that of climate,with relative contributions of 59.07%and40.93%,respectively.However,the degradation of vegetation was mainly caused by human actions,with a relative contribution of71.19%.Partial correlation analysis showed that vegetation was greatly affected by temperature(r=0.62,P<0.05)due to the relatively sufficient moisture but lower temperature in TRSR.Furthermore,the establishment of nature reserves and the implementation of the Ecological Protection and Restoration Program(EPRP)improved vegetation,and the first stage EPRP had a better effect on vegetation restoration than the second stage.Our findings identify the driving factors of vegetation change and lay the foundation for subsequent effective management.

Analysis of Climate Variability and Relation to Vegetation in Garamba National Park from 1990-2020

Climatic factors impact vegetation. Our study was to examine and analyze the climate variability and relationship to vegetation in Garamba National Park of the Democratic Republic of the Congo over the past 30 years (1990 to 2020), then to relate the climatic variables. Mann Kendall’s non parametric test, ANOVA, and p-value tests are used to analyze existing trends and relationships between vegetation cover, climatic factors, land surface temperature (LST) and normalized difference in temperature Vegetation index (NDVI), Enhanced vegetation index (EVI) in Garamba national park which is of particular importance for the network of protected areas of the Democratic republic of Congo because its position at the northern limit of the savanna-forest mosaics gives it a unique biodiversity. The southern part of the park is dominated by grassy shrub savannas. The results showed that: 1) In Garamba, the monthly correlation coefficient of Kendall and Pearsan between temperature and precipitation are negative respectively 0.763 and −0.876 (p-value < 0.00001). 2) Annually during the three decades in Garamba, the correlation between precipitation and NDVI is significant 0.416 (Kendall) and 0.496 (Pearsan);the same between precipitation and EVI 0.291 (Kendall) and 0.496 (Pearsan) while LST and precipitation are negatively correlated (p-value < 0.00001).

Vegetation recovery trends under dual dominance of climate change and anthropogenic factors in the severely damaged areas of the Wenchuan earthquake

The occurrence of the Wenchuan earthquake caused the degradation of regional ecosystems,including vegetation destruction.However,the post-seismic vegetation recovery and its driving forces on the spatial-temporal scale are still vague,especially in the severely damaged areas(including Wenchuan,Beichuan,Mianzhu,Shifang,Qingchuan,Maoxian,Anzhou,Dujiangyan,Pingwu and Pengzhou).Here,we detected vegetation recovery in the severely damaged areas by using Ensemble Empirical Mode Decomposition(EEMD)to analyze the time series characteristics of the Enhanced Vegetation Index(EVI),and explored the driving effects of climate,land use types,nighttime light,water system,slope,and clay content on vegetation recovery based on Geographically and Temporally Weighted Regression(GTWR)model.The results indicated that the post-seismic vegetation recovery rate increased rapidly(acceleration>0)but slowed down after 2013.And the areas of best vegetation recovery(EVI increments>0.1)were distributed in the north of the study area,the Minjiang River Basin,and front fault and central fault of the Longmenshan Fault Zone.While the areas with the worst vegetation recovery(EVI increments<-0.1)were concentrated in the southern high-altitude areas and the Chengdu Plain.Additionally,a process attribution of the driving forces of vegetation recovery indicated that accumulated precipitation and maximum temperature promoted vegetation recovery(regression coefficients>0),but the impacts weakened after the earthquake,possibly due to the increase of secondary disasters induced by precipitation and the rise in maximum temperature.The impact of cultivated land on vegetation recovery was mostly positive(regression coefficients>0),which may be related to the implementation of the Grain for Green Project.The nighttime light inhibited vegetation recovery(regression coefficients<0),which could be closely associated with urbanization.The results indicated that more attention should be paid to the nonlinear variations of post-earthquake vegetation recovery trends,and the effects of climatic and anthropogenic factors on vegetation recovery also should not be underestimated.

Change of Vegetation Cover and Its Relationships with Climatic Factors in Large-area Eucalyptus Introduced Region of Yunnan

Taking Lancang County as a study area with a large area of eucalyptus introduction in Yunnan, spatiotemporal change characteristics of vegetation cover, as well as the relationships between Enhanced Vegetation Index（EVl） and climatic factors （temperature and precipitation） were analyzed by using the data of MODIS-EVI from 2005 to 2010. The results indicated that： （1） The vegetation cover was overall good, and the annual average values of EVl were greater than 0.395 and showed a slow increasing trend from 2005 to 2010 in study area; the monthly average values of EVl ranged from 0.296 to 0.538, and seasonal variability was obvious. Monthly average values of EVl usually fell to the lowest level in February and March, and reached the peak in July and August. From the perspective of space, average EVl over the years significantly varied in different towns of Lancang County. During 2005 -2010, in 92.534% area of total, vegetation coverage change were not obvious; in 7.25% area of total, vegeta- tion becoming better; only in 0.02% area of total, vegetation cover were getting worse. （2） Monthly average values of EVl were significantly correlated with monthly average rainfall in Lancang County. The maxima of monthly average EVI and rainfall appeared in August on summer, while the minima of monthly average EVl and rainfall appeared in February and January on winter respectively. （3） Monthly average EVl was somewhat relative with monthly average temperature. The maxima of monthly average EVl and temperature appeared in June and August respectively, while the minima appeared in January and February respectively.

Monitoring Perennial Sub-Surface Waterlogged Croplands Based on MODIS in Jianghan Plain, Middle Reaches of the Yangtze River

Perennial waterlogged soil（PWS） is induced by the high level of groundwater, and has a persistent impact on natural ecosystems and agricultural production. Traditionally, distribution information regarding PWS is mainly collected from in situ measurements through groundwater level surveys and physicochemical property analyses. However, in situ measurements of PWS are costly and time-consuming, only rough estimates of PWS areas are available in some regions. In this paper, we developed a method to monitor the perennial waterlogged cropland using time-series moderate resolution imaging spectroradiometer（MODIS） data. The Jianghan Plain, a floodplain located in the middle reaches of the Yangtze River, was selected as the study area. Temporal variations of the enhanced vegetation index（EVI）, night land surface temperature（LST）, diurnal LST differences（ΔLST）, albedo, and the apparent thermal inertia（ATI） were used to analyze the ecological and thermodynamic characteristics of the waterlogged croplands. To obtain pure remote sensing signatures of the waterlogged cropland from mixed pixels, the croplands were classified into different types according to soil and land cover types in this paper, and a linear mixing model was developed by fitting the signatures using the multiple linear regression approach. Afterwards, another linear spectral mixing model was used to get the proportions of waterlogged croplands in each 1 km×1 km pixel. The result showed an acceptable accuracy with a root-mean-square error of 0.093. As a tentative method, the procedure described in this paper works efficiently as a method to monitor the spatial patterns of perennial sub-surface waterlogged croplands at a wide scale.

Geographic range size patterns across plants and animals of Xinjiang,China

Patterns in species geographic range size are relatively well-known for vertebrates,but still poorly known for plants.Contrasts of these patterns between groups have rarely been investigated.With a detailed flora and fauna distribution database of Xinjiang,China,we used regression methods,redundancy analysis and random forests to explore the relationship of environment and body size with the geographic range size of plants,mammals and birds in Xinjiang and contrast these patterns between plants and animals.We found positive correlations between species range size and body size.The range size of plants was more influenced by water variables,while that of mammals and birds was largely influenced by temperature variables.The productivity variable,i.e.,Enhanced Vegetation Index(EVI)was far more correlated with range size than climatic variables for both plants and animals,suggesting that vegetation productivity inferred from remote sensing data may be a good predictor of species range size for both plants and animals.

The Influence of Tropical Cyclones to the Plant Productivity Indices along the Coast of Tanzania

The study investigated the influence of Tropical cyclone (TCs) to the plant productivity indices along the coast of Tanzania using both field observations and change detection methods. These indices are normally designed to maximize the sensitivity of the vegetation characteristics and are very crucial in monitoring droughts intensity, yield and biomass amongst others. The study used three types of satellite imageries including the 16 days Moderate Resolution Imaging Spectroradiometer (MODIS) of 250 × 250 m resolution;8 days Landsat 7 enhanced thematic mapper (ETM) with resolution of 30 × 30 m composites, and 5 Landsat 8 (LC8) images, to determine the patterns and the variability of the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) and TCs impacts on vegetation. Moreover, we used Tropical Rainfall Measuring Mission (TRMM) data and the daily to monthly rainfall data from Tanzanian Meteorological Authority (TMA). The change detection between the pre and post storm (TCs) conditions was used to analyse inter annual variability of EVI over Chwaka, Rufiji and Pugu— Kazimzumbwi. The changes in NDVI and EVI and monthly rainfall at the coastal stations were calculated, plotted and analyzed. The results revealed that, highest EVI values over coastal Tanzania were observed during March and April, and minimum (low) values in November. The results for EVI changes based on pre and post storm conditions revealed that most observed stations and most TCs led to significant EVI changes which ranged from -0.05 to 0.19, and -0.3 to 0.22, for MODIS and L7 ETM data, respectively. As for the spatial changes in NDVI results revealed that, TCs (Besija and Fobane) were associated with positive NDVI changes i.e. (enhancement) of >0.51 and >0.31, and NDVI reduction (i.e. negative changes) of <0.02 and <-0.19 for Chwaka and Rufiji, respectively. Besides the results revealed that, TCs episodes have induced a land cover changes from i.e. water covered areas changed to be vegetation covered especially over the shorelines and inter tidal areas. Indeed, these results were consistent with the analysis of rainfall patterns which indicated that low rainfall occurred in low NDVI areas and vice versa.

Monitoring of Carbon Monoxide (CO) changes in the atmosphere and urban environmental indices extracted from remote sensing images for 932 Iran cities from 2019 to 2021

Carbon Monoxide(CO)is an important urban pollutant with a relation to 5,transition economies based on emission intensities.In this study,Sentinel-MODerate resolution Imaging Spectroradiometer(MODIS),and Landsat-8 images were used to investigate the variations of CO and urban environmental indices and the correlations between them.From the assessed correlations for 932 Iranian cities,it occurred that the assessed indices were all correlated.The highest CO levels were 0.031 in the spring of 2019 and 2020,whereas in 2021 it was equal to 0.030 in both the spring and winter,respectively.In 2019 and 2020 the maximum values of the Enhanced Vegetation Index(EVI)in the spring were 0.181 and 0.183.Exceptionally high Absorbing Aerosol Index(AAI)values of–0.834 and–1.0,along with Urban Index(UI)of 0.102 and 0.092,were correlated with recorded spikes in CO level,despite that these seasons’EVI values were not so abnormal.It was forecasted that in 2030 rises in the CO level by 13.2%in the winter and by 17.5%in the fall are expected,with the simultaneous increase of AAI by 204.5%and 980.2%,and Aerosol Optical Depth(AOD)by 27%and 5%in the winter and spring,respectively.

Variations of root and heterotrophic respiration along environmental gradients in China’s forests

Aims Root and heterotrophic respiration may respond differently to environmental variability,but little evidence is available from largescale observations.Here we aimed to examine variations of root and heterotrophic respiration across broad geographic,climatic,soil and biotic gradients.Methods We conducted a synthesis of 59 field measurements on root and heterotrophic respiration across China’s forests.Important Findings Root and heterotrophic respiration varied differently with forest types,of which evergreen broadleaf forest was significantly different from those in other forest types on heterotrophic respiration but without statistically significant differences on root respiration.The results also indicated that root and heterotrophic respiration exhibited similar trends along gradients of precipitation,soil organic carbon and satellite-indicated vegetation growth.However,they exhibited different relationships with temperature:root respiration exhibited bimodal patterns along the temperature gradient,while heterotrophic respiration increased monotonically with temperature.Moreover,they showed different relationships with MOD17 GPP,with increasing trend observed for root respiration whereas insignificant change for heterotrophic respiration.In addition,root and heterotrophic respiration exhibited different changes along the age sequence,with insignificant change for root respiration and decreasing trend for heterotrophic respiration.Overall,these results suggest that root and heterotrophic respiration may respond differently to environmental variability.Our findings could advance our understanding on the different environmental controls of root and heterotrophic respiration and also improve our ability to predict soil CO_(2) flux under a changing environment.

Multi-year monitoring of paddy rice planting area in Northeast China using MODIS time series data

The objective of this study was to investigate the tempo-spatial distribution of paddy rice in Northeast China using moderate resolution imaging spectroradiometer(MODIS) data.We developed an algorithm for detection and estimation of the transplanting and flooding periods of paddy rice with a combination of enhanced vegetation index(EVI) and land surface water index with a central wavelength at 2130 nm(LSWI2130).In two intensive sites in Northeast China,fine resolution satellite imagery was used to validate the performance of the algorithm at pixel and 3×3 pixel window levels,respectively.The commission and omission errors in both of the intensive sites were approximately less than 20%.Based on the algorithm,annual distribution of paddy rice in Northeast China from 2001 to 2009 was mapped and analyzed.The results demonstrated that the MODIS-derived area was highly correlated with published agricultural statistical data with a coefficient of determination(R2) value of 0.847.It also revealed a sharp decline in 2003,especially in the Sanjiang Plain located in the northeast of Heilongjiang Province,due to the oversupply and price decline of rice in 2002.These results suggest that the approaches are available for accurate and reliable monitoring of rice cultivated areas and variation on a large scale.

Synergy of remotely sensed data in spatiotemporal dynamic modeling of the crop and cover management factor

Soil erosion is a threat to the water quality constituents of sediments and nutrients and can cause long-term environmental damages.One important parameter to quantify the risk of soil loss from erosion is the crop and cover management factor(C-factor),which represents how cropping and management practices affect the rates and potential risk of soil erosion.We developed remotely sensed data-driven models for dynamic predictions of C-factor by implementing dynamic land cover modeling using the SWAT(Soil and Water Assessment Tool)model on a watershed scale.The remotely sensed processed variables included the enhanced vegetation index(EVI),the fraction of photosynthetically active radiation absorbed by green vegetation(FPAR),leaf area index(LAI),soil available water content(AWC),slope gradient(SG),and ratio of area(AR)of every hydrologic response unit(HRU)to that of the total watershed,comprising unique land cover,soil type,and slope gradient characteristics within the Fish River catchment in Alabama,USA between 2001 and 2014.Linear regressions,spatial trend analysis,correlation matrices,forward stepwise multivariable regression(FSMR),and 2-fold cross-validation were conducted to evaluate whether there were possible associations between the C-factor and EVI with the successive addition of remotely sensed environmental factors.Based on the data analysis and modeling,we found a significant association between the C-factor and EVI with the synergy of the environmental factors FPAR,LAI,AWC,AR,and SG(predicted R^(2)(R^(2)_(pred))=0.51;R^(2)=0.68,n=3220,P<0.15).The results showed that the developed FSMR model constituting the non-conventional factors AWC(R^(2)_(pred)=0.32;R^(2)=0.48,n=3220,P<0.05)and FPAR(R^(2)_(pred)=0.13;R^(2)=0.28,n=3220,P=0.31)was an improved fit for the watershed C-factor.In conclusion,the union of dynamic variables related to vegetation(EVI,FPAR,and LAI),soil(AWC),and topography(AR and SG)can be utilized for spatiotemporal C-factor estimation and to monitor watershed erosion.

Mapping fractional cropland covers in Brazil through integrating LSMA and SDI techniques applied to MODIS imagery

MODIS time-series imagery is promising for generating regional and global land cover products.For Brazil,however,accurate fractional cropland covers(FCC)information is difficult to obtain due to frequent cloud coverage and the mixing-pixel problem.To address these problems,this study developed an innovative approach to mapping the FCC of the Mato Grosso State,Brazil through integrating Linear Spectral Mixture Analysis(LSMA)and Seasonal Dynamic Index(SDI)models.With MOD13Q1 time-series EVI imagery,a SDI was developed to represent the phenology of croplands.Furthermore,fractional land covers(e.g.,vegetation,soil,and low albedo components)were derived with the LSMA algorithms.A stepwise regression model was established to estimate the FCC at the regional scale.Finally,ground truth cropland cover information was extracted from Landsat TM imagery using a hybrid method.Results indicated that the combination of multiple feature variables produced better results when compared with individual variables.Through cross-validation and comparative analysis,the coefficient of determination(R^(2))between the reference and estimated FCCs reached 0.84 with a Root Mean Square Error(RMSE)of 0.13.This indicates that the proposed method effectively improved the accuracy of fractional cropland mapping.When compared to the traditional per-pixel“hard”classification,the sub-pixel level maps illustrated detailed cropland spatial distribution patterns.

Applicability of remote sensingbased surface temperature regimes in determining deciduous phenology over boreal forest

Aims The study of deciduous phenology over boreal forest is important for understanding forest ecology and better management.In this paper,our objective was to determine the phenological stages of deciduous leaf out(DLO)over the deciduous-dominant[i.e.trembling aspen(Populus tremuloides)]stands in the Canadian Province of Alberta.Methods During the period 2006–08,we used Moderate Resolution Imaging Spectroradiometer(MODIS)-based 8-day surface temperature(TS)images to calculate accumulated growing degree days(AGDD:a favourable temperature regime for plant growth).The temporal dynamics of AGDD in conjunction with in situ DLO observations were then analysed in determining the optimal threshold for DLO in 2006(i.e.80 degree days).Important Findings The implementation of the above-mentioned optimal threshold revealed reasonable agreements(i.e.on an average 91.9%of the DLO cases within ±2 periods or ±16 days of deviations during 2007–08)in comparison to the in situ observed data.The developments could be useful in various forestry-related applications,e.g.plant growth and its ability of exchanging atmospheric carbon dioxide,forest ecohydrology,risk of insect infestation,forest fire and impact of climate change,among others.

The improvement of an object-oriented classification using multi-temporal MODIS EVI satellite data

This paper investigates the contribution of multi-temporal enhanced vegetation index(EVI)data to the improvement of object-based classification accuracy using multi-spectral moderate resolution imaging spectral-radiometer(MODIS)imagery.In object-oriented classification,similar pixels are firstly grouped together and then classified;the produced result does not suffer the speckled appearance and closer to human vision.EVI data are from the MODIS sensor aboard Terra spacecraft.69 EVI data(scenes)were collected during the period of three years(2001
2003)in a mountainous vegetated area.These data sets were used to study the phenology of the land cover types.Different land cover types show distinct fluctuations over time in EVI values and this information might be used to improve object-oriented land cover classification.Two experiments were carried out:one was only with single date MODIS multispectral data,and the other one including also the 69 EVI images.Eight classes were distinguished:temperate forest,tropical dry forest,grassland,irrigated agriculture,rain-fed agriculture,orchards,lava flows and human settlement.The two classifications were evaluated with independent verification data,and the results showed that with multitemporal EVI data,the classification accuracy was improved 5.2%.Evaluated by McNemar’s test,this improved was significant,with significance level p=0.01.