A phenology-based vegetation index for improving ratoon rice mapping using harmonized Landsat and Sentinel-2 data

Ratoon rice,which refers to a second harvest of rice obtained from the regenerated tillers originating from the stubble of the first harvested crop,plays an important role in both food security and agroecology while requiring minimal agricultural inputs.However,accurately identifying ratoon rice crops is challenging due to the similarity of its spectral features with other rice cropping systems(e.g.,double rice).Moreover,images with a high spatiotemporal resolution are essential since ratoon rice is generally cultivated in fragmented croplands within regions that frequently exhibit cloudy and rainy weather.In this study,taking Qichun County in Hubei Province,China as an example,we developed a new phenology-based ratoon rice vegetation index(PRVI)for the purpose of ratoon rice mapping at a 30 m spatial resolution using a robust time series generated from Harmonized Landsat and Sentinel-2(HLS)images.The PRVI that incorporated the red,near-infrared,and shortwave infrared 1 bands was developed based on the analysis of spectro-phenological separability and feature selection.Based on actual field samples,the performance of the PRVI for ratoon rice mapping was carefully evaluated by comparing it to several vegetation indices,including normalized difference vegetation index(NDVI),enhanced vegetation index(EVI)and land surface water index(LSWI).The results suggested that the PRVI could sufficiently capture the specific characteristics of ratoon rice,leading to a favorable separability between ratoon rice and other land cover types.Furthermore,the PRVI showed the best performance for identifying ratoon rice in the phenological phases characterized by grain filling and harvesting to tillering of the ratoon crop(GHS-TS2),indicating that only several images are required to obtain an accurate ratoon rice map.Finally,the PRVI performed better than NDVI,EVI,LSWI and their combination at the GHS-TS2 stages,with producer's accuracy and user's accuracy of 92.22 and 89.30%,respectively.These results demonstrate that the proposed PRVI based on HLS data can effectively identify ratoon rice in fragmented croplands at crucial phenological stages,which is promising for identifying the earliest timing of ratoon rice planting and can provide a fundamental dataset for crop management activities.

Improving the Accuracy of Vegetation Index Retrieval for Biomass by Combining Ground-UAV Hyperspectral Data-A New Method for Inner Mongolia Typical Grasslands

Grassland biomass is an important parameter of grassland ecosystems.The complexity of the grassland canopy vegetation spectrum makes the long-term assessment of grassland growth a challenge.Few studies have explored the original spectral information of typical grasslands in Inner Mongolia and examined the influence of spectral information on aboveground biomass(AGB)estimation.In order to improve the accuracy of vegetation index inversion of grassland AGB,this study combined ground and Unmanned Aerial Vehicle(UAV)remote sensing technology and screened sensitive bands through ground hyperspectral data transformation and correlation analysis.The narrow band vegetation indices were calculated,and ground and airborne hyperspectral inversion models were established.Finally,the accuracy of the model was verified.The results showed that:(1)The vegetation indices constructed based on the ASD FieldSpec 4 and the UAV were significantly correlated with the dry and fresh weight of AGB.(2)The comparison between measured R^(2) with the prediction R^(2) indicated that the accuracy of the model was the best when using the Soil-Adjusted Vegetation Index(SAVI)as the independent variable in the analysis of AGB(fresh weight/dry weight)and four narrow-band vegetation indices.The SAVI vegetation index showed better applicability for biomass monitoring in typical grassland areas of Inner Mongolia.(3)The obtained ground and airborne hyperspectral data with the optimal vegetation index suggested that the dry weight of AGB has the best fitting effect with airborne hyperspectral data,where y=17.962e^(4.672x),the fitting R^(2) was 0.542,the prediction R^(2)was 0.424,and RMSE and REE were 57.03 and 0.65,respectively.Therefore,established vegetation indices by screening sensitive bands through hyperspectral feature analysis can significantly improve the inversion accuracy of typical grassland biomass in Inner Mongolia.Compared with ground monitoring,airborne hyperspectral monitoring better reflects the inversion of actual surface biomass.It provides a reliable modeling framework for grassland AGB monitoring and scientific and technological support for grazing management.

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

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

Interannual Variability of the Normalized Difference Vegetation Index on the Tibetan Plateau and Its Relationship with Climate Change

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.

New Vegetation Index and Its Application in Estimating Leaf Area Index of Rice

Leaf area index （LAI） is an important characteristic of land surface vegetation system, and is also a key parameter for the models of global water balancing and carbon circulation. By using the reflectance values of Landsat-5 blue, green and red channels simulated from rice reflectance spectrum, the sensitivities of the bands to LAI were analyzed, and the response and capability to estimate LAI of various NDVIs （normalized difference vegetation indices）, which were established by substituting the red band of general NDVI with all possible combinations of red, green and blue bands, were assessed. Finally, the conclusion was tested by rice data at different conditions. The sensitivities of red, green and blue bands to LAI were different under various conditions. When LAI was less than 3, red and blue bands were more sensitive to LAI. Though green band in the circumstances was less sensitive to LAI than red and blue bands, it was sensitive to LAI in a wider range. When the vegetation indices were constituted by all kinds of combinations of red, green and blue bands, the premise for making the sensitivity of these vegetation indices to LAI be meaningful was that the value of one of the combinations was greater than 0.024, i.e. visible reflectance （VIS）〉0.024. Otherwise, the vegetation indices would be saturated, resulting in lower estimation accuracy of LAI. Comparison on the capabilities of the vegetation indices derived from all kinds of combinations of red, green and blue bands to LAI estimation showed that GNDVI （Green NDVI） and GBNDVI （Green-Blue NDVI） had the best relations with LAI. The capabilities of GNDVI and GBNDVI to LAI estimation were tested under different circumstances, and the same result was acquired. It suggested that GNDVI and GBNDVI performed better to predict LAI than the conventional NDVI.

Numerical Simulation of the Impact of Vegetation Index on the Interannual Variation of Summer Precipitation in the Yellow River Basin

Two sets of numerical experiments using the coupled National Center for Environmental Prediction General Circulation Model （NCEP/GCM T42L18） and the Simplified Simple Biosphere land surface scheme （SSiB） were carried out to investigate the climate impacts of fractional vegetation cover （FVC） and leaf area index （LAI） on East Asia summer precipitation, especially in the Yellow River Basin （YRB）. One set employed prescribed FVC and LAI which have no interannual variations based on the climatology of vegetation distribution; the other with FVC and LAI derived from satellite observations of the International Satellite Land Surface Climate Project （ISLSCP） for 1987 and 1988. The simulations of the two experiments were compared to study the influence of FVC, LAI on summer precipitation interannual variation in the YRB. Compared with observations and the NCEP reanalysis data, the experiment that included both the effects of satellite-derived vegetation indexes and sea surface temperature （SST） produced better seasonal and interannual precipitation variations than the experiment with SST but no interannual variations in FVC and LAI, indicating that better representations of the vegetation index and its interannual variation may be important for climate prediction. The difference between 1987 and 1988 indicated that with the increase of FVC and LAI, especially around the YRB, surface albedo decreased, net surface radiation increased, and consequently local evaporation and precipitation intensified. Further more, surface sensible heat flux, surface temperature and its diurnal variation decreased around the YRB in response to more vegetation. The decrease of surface-emitting longwave radiation due to the cooler surface outweighed the decrease of surface solar radiation income with more cloud coverage, thus maintaining the positive anomaly of net surface radiation. Further study indicated that moisture flux variations associated with changes in the general circulation also contributed to the precipitation interannual variation.

Simple method for extracting the seasonal signals of photochemical reflectance index and normalized difference vegetation index measured using a spectral reflectance sensor

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.

Vegetation Index Reconstruction and Linkage with Drought for the Source Region of the Yangtze River Based on Tree-ring Data

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.

Drought trend analysis in a semi-arid area of Iraq based on Normalized Difference Vegetation Index, Normalized Difference Water Index and Standardized Precipitation Index

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.

Inversion of Biochemical Parameters by Selection of Proper Vegetation Index in Winter Wheat

Recent studies have demonstrated the application of vegetation indices from canopy reflectedspectrum for inversion of chlorophyll concentration. Some indices are both response tovariations of vegetation and environmental factors. Canopy chlorophyll concentration, anindicator of photosynthesis activity, is related to nitrogen concentration in green vegetationand serves as an indicator of the crop response to soil nitrogen fertilizer application. Thecombination of normalized difference vegetation index (NDVI) and photochemical reflectanceindex (PRI) can reduce the effect of leaf area index (LAI) and soil background. The canopychlorophyll inversion index (CCII) was proved to be sensitive to chlorophyll concentration andvery resistant to the other variations. This paper introduced the ratio of TCARI/OSAVI to makeaccurate predictions of winter wheat chlorophyll concentration under different cultivars. Itindicated that canopy chlorophyll concentration could be evaluated by some combined vegetationindices.

Soil Moisture Monitoring Based on Land Surface Temperature-Vegetation Index Space Derived from MODIS Data

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.

Response of vegetation variation to climate change and human activities in the Shiyang River Basin of China during 2001-2022

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.

A shadow- eliminated vegetation index (SEVI) for removal of self and cast shadow effects on vegetation in rugged terrains

The effect of terrain shadow, including the self and cast shadows, is one ofthe main obstacles for accurate retrieval of vegetation parameters byremote sensing in rugged terrains. A shadow- eliminated vegetation index(SEVI) was developed, which was computed from only red and nearinfrared top-of-atmosphere reflectance without other heterogeneous dataand topographic correction. After introduction of the conceptual modeland feature analysis of conventional wavebands, the SEVI was constructedby ratio vegetation index (RVI), shadow vegetation index (SVI) andadjustment factor (f (Δ)). Then three methods were used to validate theSEVI accuracy in elimination of terrain shadow effects, including relativeerror analysis, correlation analysis between the cosine of solar incidenceangle (cosi) and vegetation indices, and comparison analysis between SEVIand conventional vegetation indices with topographic correction. Thevalidation results based on 532 samples showed that the SEVI relativeerrors for self and cast shadows were 4.32% and 1.51% respectively. Thecoefficient of determination between cosi and SEVI was only 0.032 and thecoefficient of variation (std/mean) for SEVI was 12.59%. The results indicatethat the proposed SEVI effectively eliminated the effect of terrain shadowsand achieved similar or better results than conventional vegetation indiceswith topographic correction.

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.

A Modified Temperature-Vegetation Dryness Index(MTVDI)for Assessment of Surface Soil Moisture Based on MODIS Data

Spatio-temporal dynamic monitoring of soil moisture is highly important to management of agricultural and vegetation eco-systems.The temperature-vegetation dryness index based on the triangle or trapezoid method has been used widely in previous studies.However,most existing studies simply used linear regression to construct empirical models to fit the edges of the feature space.This requires extensive data from a vast study area,and may lead to subjective results.In this study,a Modified Temperature-Vegetation Dryness Index(MTVDI)was used to monitor surface soil moisture status using MODIS(Moderate-resolution Imaging Spectroradiometer)remote sensing data,in which the dry edge conditions were determined at the pixel scale based on surface energy balance.The MTVDI was validated by field measurements at 30 sites for 10 d and compared with the Temperature-Vegetation Dryness Index(TVDI).The results showed that the R^(2) for MTVDI and soil moisture obviously improved(0.45 for TVDI,0.69 for MTVDI).As for spatial changes,MTVDI can also better reflect the actual soil moisture condition than TVDI.As a result,MTVDI can be considered an effective method to monitor the spatio-temporal changes in surface soil moisture on a regional scale.

Monitoring vegetation drought in the nine major river basins of China based on a new developed Vegetation Drought Condition Index

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.

Seasonal Variation and Land-Use/Land-Cover Type Impacts on the Correlation of Urban Heat Island Intensity and Difference Vegetation Index with Satellite Data in Xi’an,China

Green coverage has pronounced influences on urban heat island（UHI） effect, while the impacts of seasonal variation and Land-Use/Land-Cover（LULC） types on this effect has not been implemented. This paper investigated the spatio-seasonal characteristics of urban thermal environment and the vegetation-soil mixed area, and then explored the effects of vegetation status on UHI intensity from the perspectives of seasons and regions in Xi＇an using four Landsat 8 images. UHI intensity index was implemented to extract UHI intensity based on thermal infrared imagery, and difference vegetation index（DVI） was used to represent vegetation-soil mixed area. Results indicated that DVI has impacts on UHI intensity, and their relations vary with season and region. In the whole Xi＇an, if UHI intensity is smaller than-0.1, DVI increases with the increase of UHI intensity; whereas for UHI intensity is greater than-0.1, DVI decreases with increases of the UHI intensity from early spring to autumn. The highest correlation level was discovered in the autumn map（R^2=0.713）. Results of correlation analysis further displayed that DVI positively correlated with UHI intensity at impervious surface, and that the main urban area possessed the best correlation with R^2=0.564 5.

A new vegetation index combination for leaf carotenoid-tochlorophyll ratio: minimizing the effect of their correlation

The ratio of leaf carotenoid to chlorophyll(Car/Chl)is an indicator of vegetation photosynthesis,development and responses to stress.However,the correlation between Car and Chl,and their overlapping absorption in the visible spectral domain pose a challenge for optical remote sensing of their ratio.This study aims to investigate combinations of vegetation indices(VIs)to minimize the influence of Car-Chl correlation,thus being more sensitive to the variability in the ratio across vegetation species and sites.VIs sensitive to Car and Chl variability were combined into four candidates of combinations,using a simulated dataset from the PROSPECT model.The VI combinations were then tested using six simulated datasets with different Car-Chl correlations,and evaluated against four independent datasets.The ratio of the carotenoid triangle ratio index(CTRI)with the red-edge chlorophyll index(CIred-edge)was found least influenced by the Car-Chl correlation and demonstrated a superior ability for estimating Car/Chl variability.Compared with published VIs and two machine learning algorithms,CTRI/CIred-edge also showed the optimal performance in the fourfield datasets.This new VI combination could be useful to provide insights in spatiotemporal variability in the leaf Car/Chl ratio,applicable for assessing vegetation physiology,phenology,and response to environmental stress.

Correlation analysis of Normalized Different Vegetation Index(NDVI)difference series and climate variables in the Xilingole steppe,China from 1983 to 1999

There is a crucial need in the study of global change to understand how terrestrial ecosystems respond to the climate system.It has been demonstrated by many researches that Normalized Different Vegetation Index(NDVI)time series from remotely sensed data,which provide effective information of vegetation conditions on a large scale with highly temporal resolution,have a good relation with meteorological factors.However,few of these studies have taken the cumulative property of NDVI time series into account.In this study,NDVI difference series were proposed to replace the original NDVI time series with NDVI difference series to reappraise the relationship between NDVI and meteorological factors.As a proxy of the vegetation growing process,NDVI difference represents net primary productivity of vegetation at a certain time interval under an environment controlled by certain climatic conditions and other factors.This data replacement is helpful to eliminate the cumulative effect that exist in original NDVI time series,and thus is more appropriate to understand how climate system affects vegetation growth in a short time scale.By using the correlation analysis method,we studied the relationship between NOAA/AVHRR ten-day NDVI difference series and corresponding meteorological data from 1983 to 1999 from 11 meteorological stations located in the Xilingole steppe in Inner Mongolia.The results show that:(1)meteorological factors are found to be more significantly correlation with NDVI difference at the biomass-rising phase than that at the falling phase;(2)the relationship between NDVI difference and climate variables varies with vegetation types and vegetation communities.In a typical steppe dominated by Leymus chinensis,temperature has higher correlation with NDVI difference than precipitation does,and in a typical steppe dominated by Stipa krylovii,the correlation between temperature and NDVI difference is lower than that between precipitation and NDVI difference.In a typical steppe dominated by Stipa grandis,there is no significant difference between the two correlations.Precipitation is the key factor influencing vegetation growth in a desert steppe,and temperature has poor correlation with NDVI dif-ference;(3)the response of NDVI difference to precipitation is fast and almost simultaneous both in a typical steppe and desert steppe,however,mean temperature exhibits a time-lag effect especially in the desert steppe and some typical steppe dominated by Stipa krylovii;(4)the relationship between NDVI difference and temperature is becoming stronger with global warming.

Feature extraction and analysis of reclaimed vegetation in ecological restoration area of abandoned mines based on hyperspectral remote sensing images

The vegetation growth status largely represents the ecosystem function and environmental quality.Hyperspectral remote sensing data can effectively eliminate the effects of surface spectral reflectance and atmospheric scattering and directly reflect the vegetation parameter information.In this study,the abandoned mining area in the Helan Mountains,China was taken as the study area.Based on hyperspectral remote sensing images of Zhuhai No.1 hyperspectral satellite,we used the pixel dichotomy model,which was constructed using the normalized difference vegetation index(NDVI),to estimate the vegetation coverage of the study area,and evaluated the vegetation growth status by five vegetation indices(NDVI,ratio vegetation index(RVI),photochemical vegetation index(PVI),red-green ratio index(RGI),and anthocyanin reflectance index 1(ARI1)).According to the results,the reclaimed vegetation growth status in the study area can be divided into four levels(unhealthy,low healthy,healthy,and very healthy).The overall vegetation growth status in the study area was generally at low healthy level,indicating that the vegetation growth status in the study area was not good due to short-time period restoration and harsh damaged environment such as high and steep rock slopes.Furthermore,the unhealthy areas were mainly located in Dawukougou where abandoned mines were concentrated,indicating that the original mining activities have had a large effect on vegetation ecology.After ecological restoration of abandoned mines,the vegetation coverage in the study area has increased to a certain extent,but the amplitude was not large.The situation of vegetation coverage in the northern part of the study area was worse than that in the southern part,due to abandoned mines mainly concentrating in the northern part of the Helan Mountains.The combination of hyperspectral remote sensing data and vegetation indices can comprehensively extract the characteristics of vegetation,accurately analyze the plant growth status,and provide technical support for vegetation health evaluation.