Drought Impacts on Vegetation Indices and Productivity of Terrestrial Ecosystems in Southwestern China During 2001–2012

Drought, as a recurring extreme climate event, affects the structure, function, and process of terrestrial ecosystems. Despite the increasing occurrence and intensity of the drought in the past decade in Southwestern China, the impacts of continuous drought events on vegetation in this region remain unclear. During 2001–2012, Southwestern China experienced the severe drought events from 2009 to 2011. Our aim is to characterize drought conditions in the Southwestern China and explore the impacts on the vegetation condition and terrestrial ecosystem productivity. The Standardized Precipitation Index(SPI) was used to characterize drought area and intensity and a light-use efficiency model was used to explore the effect of drought on the terrestrial ecosystem productivity with Moderate Resolution Imaging Spectrometer(MODIS) data. The SPI captured the major drought events in Southwestern China during the study period, indicated that the 12-year period of this study included both ‘normal' precipitation years and two severe drought events in 2009–2010 and 2011. Results showed that vegetation greenness(Normalized Difference Vegetation Index, NDVI and Enhanced Vegetation Index, EVI) both declined in 2009/2010 drought, but the 2011 drought resulted in less declines of vegetation greenness and productivity due to shorten drought duration and rising temperature. Meanwhile, it was about 5 months lapse between drought events and maximum declines in vegetation greenness for 2009/2010 drought events. In addition, forest, grassland and cropland revealed significant different ecosystem responses to drought. It indicated that grassland showed an early sensitivity to drought, while cropland was the most sensitive to water deficit and forest was more resilient to drought. This study suggests that it is necessary to detect the difference responses of ecosystem to drought in a regional area with satellite data and ecosystem model.

Estimating aboveground biomass in Mu Us Sandy Land using Landsat spectral derived vegetation indices over the past 30 years

Remote sensing is a valuable and effective tool for monitoring and estimating aboveground biomass （AGB） in large areas.The current international research on biomass estimation by remote sensing technique mainly focused on forests,grasslands and crops,with relatively few applications for desert ecosystems.In this paper,Landsat Thematic Mapper （TM）/Enhanced Thematic Mapper Plus （ETM＋） images from 1988 to 2007 and the data of 283 AGB samples in August 2007 were used to estimate the AGB for Mu Us Sandy Land over the past 30 years.Moreover,temporal and spatial distribution characteristics of AGB and influencing factors of climate and underlying surface were also studied.Results show that：（1） Differences of correlations exist in the fitted equations between AGB and different vegetation indices in desert areas.The modified soil adjusted vegetation index （MSAVI） and soil adjusted vegetation index （SAVI） show relatively higher correlations with AGB,while the correlation between normalized difference vegetation index （NDVI） and AGB is relatively lower.Error testing shows that the AGB-MSAVI model established can be used to accurately estimate AGB of Mu Us Sandy Land in August.（2） AGB in Mu Us Sandy Land shows the fluctuant characteristics over the past 30 years,which decreased from the 1980s to the 1990s,and increased from the 1990s to 2007.AGB in 2007 had the highest value,with a total AGB of 3.352×106 t.Moreover,in the 1990s,AGB had the lowest value with a total AGB of 2.328×106 t.（3） AGB with relatively higher values was mainly located in the middle and southern parts of Mu Us Sandy Land in the 1980s.AGB was low in the whole area in the1990s,and relatively higher AGB values were mainly located in the southern parts of Uxin.In 2007,AGB in the whole area was relatively higher than those of the last twenty years,and higher AGB values were mainly located in the northern,western and middle parts of Mu Us Sandy Land.

Deep neural network algorithm for estimating maize biomass based on simulated Sentinel 2A vegetation indices and leaf area index

Accurate estimation of biomass is necessary for evaluating crop growth and predicting crop yield.Biomass is also a key trait in increasing grain yield by crop breeding.The aims of this study were(i)to identify the best vegetation indices for estimating maize biomass,(ii)to investigate the relationship between biomass and leaf area index(LAI)at several growth stages,and(iii)to evaluate a biomass model using measured vegetation indices or simulated vegetation indices of Sentinel 2A and LAI using a deep neural network(DNN)algorithm.The results showed that biomass was associated with all vegetation indices.The three-band water index(TBWI)was the best vegetation index for estimating biomass and the corresponding R2,RMSE,and RRMSE were 0.76,2.84 t ha−1,and 38.22%respectively.LAI was highly correlated with biomass(R2=0.89,RMSE=2.27 t ha−1,and RRMSE=30.55%).Estimated biomass based on 15 hyperspectral vegetation indices was in a high agreement with measured biomass using the DNN algorithm(R2=0.83,RMSE=1.96 t ha−1,and RRMSE=26.43%).Biomass estimation accuracy was further increased when LAI was combined with the 15 vegetation indices(R2=0.91,RMSE=1.49 t ha−1,and RRMSE=20.05%).Relationships between the hyperspectral vegetation indices and biomass differed from relationships between simulated Sentinel 2A vegetation indices and biomass.Biomass estimation from the hyperspectral vegetation indices was more accurate than that from the simulated Sentinel 2A vegetation indices(R2=0.87,RMSE=1.84 t ha−1,and RRMSE=24.76%).The DNN algorithm was effective in improving the estimation accuracy of biomass.It provides a guideline for estimating biomass of maize using remote sensing technology and the DNN algorithm in this region.

Measuring cotton water status using water-related vegetation indices at leaf and canopy levels

Drought is one of the major environmental threats in the world. In recent years, the damage from droughts to the environment and economies of some countries has been extensive, and drought monitoring has caused widespread concerns. Remote sensing has a proven ability to provide spatial and temporal measurements of surface properties, and it offers an opportunity for the quantitative assessment of drought indicators such as the vegetation water content at different levels. In this study, sites of cotton field in Shihezi, Xinjiang, Northwest China were sampled. Four classical water content parameters, namely the leaf equivalent water thickness （EWT^e,f）, the fuel moisture content （FMC）, the canopy equivalent water thickness （EVVmcanopy） and vegetation water content （VWC） were evaluated against seven widely-used water-related vegetation indices, namely the NDII （normalized difference infrared index）, NDWI2130 （normalized difference water index）, NDVI （normalized difference vegetation index）, MSI （moisture stress index）, SRWI （simple ratio water index）, NOWI1240 （normalized difference water index） and WI （water index）, respectively. The results proved that the relationships between the water-related vegetation indices and EWTleaf were much better than that with FMC, and the relationships between vegetation indices and EWTcanopy were better than that with VWC. Furthermore, comparing the significance of all seven water-related vegetation in- dices, WI and NDII proved to be the best candidates for EWT detecting at leaf and canopy levels, with R2 of 0.262 and 0.306 for EWTlear-WI and EWTcanopy-NDII linear models, respectively. Besides, the prediction power of linear regression technique （LR） and artificial neural network （ANN） were compared using calibration and validation dataset, respectively. The results indicated that the performance of ANN as a predictive tool for water status meas- uring was as good as LR. The study should further our understanding of the relationships between water-related vegetation indices and water parameters.

Effects of RapidEye Imagery's Red-edge Band and Vegetation Indices on Land Cover Classification in an Arid Region

Land cover classification(LCC) in arid regions is of great significance to the assessment, prediction, and management of land desertification. Some studies have shown that the red-edge band of RapidE ye images was effective for vegetation identification and could improve LCC accuracy. However, there has been no investigation of the effects of RapidE ye images' red-edge band and vegetation indices on LCC in arid regions where there are spectrally similar land covers mixed with very high or low vegetation coverage information and bare land. This study focused on a typical inland arid desert region located in Dunhuang Basin of northwestern China. First, five feature sets including or excluding the red-edge band and vegetation indices were constructed. Then, a land cover classification system involving plant communities was developed. Finally, random forest algorithm-based models with different feature sets were utilized for LCC. The conclusions drawn were as follows: 1) the red-edge band showed slight contribution to LCC accuracy; 2) vegetation indices had a significant positive effect on LCC; 3) simultaneous addition of the red-edge band and vegetation indices achieved a significant overall accuracy improvement(3.46% from 86.67%). In general, vegetation indices had larger effect than the red-edge band, and simultaneous addition of them significantly increased the accuracy of LCC in arid regions.

Assessment of UAV Based Vegetation Indices for Nitrogen Concentration Estimation in Spring Wheat

Unmanned Aerial Vehicles (UAVs) have become increasingly popular in recent years for agricultural research. High spatial and temporal resolution images obtained with UAVs are ideal for many applications in agriculture. The objective of this study was to evaluate the performance of vegetation indices (VIs) derived from UAV images for quantification of plant nitrogen (N) concentration of spring wheat, a major cereal crop worldwide. This study was conducted at three locations in Idaho, United States. A quadcopter UAV equipped with a red edge multispectral sensor was used to collect images during the 2016 growing season. Flight missions were successfully carried out at Feekes 5 and Feekes 10 growth stages of spring wheat. Plant samples were collected on the same days as UAV image data acquisition and were transferred to lab for N concentration analysis. Different VIs including Normalized Difference Vegetative Index (NDVI), Red Edge Normalized Difference Vegetation Index (NDVIred edge), Enhanced Vegetation Index 2 (EVI2), Red Edge Simple Ratio (SRred edge), Green Chlorophyll Index (CIgreen), Red Edge Chlorophyll Index (CIred edge), Medium Resolution Imaging Spectrometer (MERIS) Terrestrial Chlorophyll Index (MTCI) and Red Edge Triangular Vegetation Index (core only) (RTVIcore) were calculated for each flight event. At Feekes 5 growth stage, red edge and green based VIs showed higher correlation with plant N concentration compare to the red based VIs. At Feekes 10 growth stage, all calculated VIs showed high correlation with plant N concentration. Empirical relationships between VIs and plant N concentration were cross validated using test data sets for each growth stage. At Feekes 5, the plant N concentration estimated based on NDVIred edge showed one to one correlation with measured N concentration. At Feekes 10, the estimated and measured N concentration were highly correlated for all empirical models, but the model based on CIgreen was the only model that had a one to one correlation between estimated and measured plant N concentration. The observed high correlations between VIs derived from UAV and the plant N concentration suggests the significance of VIs deriving from UAVs for within-season N concentration monitoring of agricultural crops such as spring wheat.

Assessment of forest dieback on the Moroccan Central Plateau using spectral vegetation indices

Cork oak forests in Morocco are rich in resources and services thanks to their great biological diversity,playing an important ecological and socioeconomic role.Considerable degradation of the forests has been accentuated in recent years by signifi cant human pressure and eff ects of climate change;hence,the health of the stands needs to be monitored.In this study,the Google Engine Earth platform was leveraged to extract the normalized diff erence vegetation index(NDVI)and soil-adjusted vegetation index,from Landsat 8 OLI/TIRS satellite images between 2015 and 2017 to assess the health of the Sibara Forest in Morocco.Our results highlight the importance of interannual variations in NDVI in forest monitoring;the variations had a signifi cantly high relationship(p<0.001)with dieback severity.NDVI was positively and negatively correlated with mean annual precipitation and mean annual temperature with respective coeffi cients of 0.49 and−0.67,highlighting its ability to predict phenotypic changes in forest species.Monthly interannual variation in NDVI between 2016 and 2017 seemed to confi rm fi eld observations of cork oak dieback in 2018,with the largest decreases in NDVI(up to−38%)in December in the most-aff ected plots.Analysis of the infl uence of ecological factors on dieback highlighted the role of substrate as a driver of dieback,with the most severely aff ected plots characterized by granite-granodiorite substrates.

Survey on Groundwater Environment Related to Vegetation in Mu Us Desert

[Objective] The research aimed to study the groundwater environment related to vegetation in Mu Us Desert.[Method] Choosing the hinterland of Mu Us Desert,the relationship between vegetation and groundwater in the desert was studied.The indicator system for the relationship between vegetation and groundwater in the sandy area was established,including vegetation population,vegetation cover,groundwater depth,vadose zone moisture content,groundwater mineralization and vadose zone salinity,as well as the corresponding field work methods.[Result] The result showed that the nine primary vegetation populations were distributed in the study area,and Artemisia,Salix and Cares were the dominant vegetation species.The groundwater mineralization in the sand dunes was 100-300mg/L,and 800mg/L in the beach,vadose zone moisture content remained at 8%-16%.The dunes salinity was less than 0.2%,and beaches were higher than 0.3%.[Conclusion] These results provided a basis for study on the relationship between vegetation and groundwater in Mu Us Desert.

Application of Vegetation Indices for Detection and Monitoring Oil Spills in Ahoada West Local Government Area of Rivers State,Nigeria

The study evaluated the environmental effects of an oil spill in Joinkrama 4 and Akimima Ahoada West LGA,Rivers State,Nigeria,using various vegetation indices.Location data for the spill were obtained from the Nigeria Oil Spill Detection and Response Agency,and Landsat imagery was acquired from the United States Geological Survey.Three soil samples were collected from the affected area,and their analysis included measuring total petroleum hydrocarbons(TPH),total hydrocarbons(THC),and polycyclic aromatic hydrocarbons(PAH).The obtained data were processed with ArcGIS software,utilizing different vegetation indices such as the Normalized Difference Vegetation Index(NDVI),Atmospheric Resistant Vegetation Index(ARVI),Soil Adjusted Vegetation Index(SAVI),Green Short Wave Infrared(GSWIR),and Green Near Infrared(GNIR).Statistical analysis was performed using SPSS and Microsoft Excel.The results consistently indicated a negative impact on the environment resulting from the oil spill.A comparison of spectral reflectance values between the oil spill site and the non-oil spill site showed lower values at the oil spill site across all vegetation indices(NDVI 0.0665-0.2622,ARVI-0.0495-0.1268,SAVI 0.0333-0.1311,GSWIR-0.183-0.0517,GNIR-0.0104--0.1980),indicating damage to vegetation.Additionally,the study examined the correlation between vegetation indices and environmental parameters associated with the oil spill,revealing significant relationships with TPH,THC,and PAH.A t-test with a significance level of p<0.05 indicated significantly higher vegetation index values at the non-oil spill site compared to the oil spill site,suggesting a potential disparity in vegetation health between the two areas.Hence,this study emphasizes the harmful effect of oil spills on vegetation and highlights the importance of utilizing vegetation indices and spectral reflectance analysis to detect and monitor the impact of oil spills on vegetation.

A Modified Transformed Soil Adjusted Vegetation Index for Cropland in Jilin Province,China

1 Introduction Vegetation indices(VIs)derived from satellite observations are an essential source of information for operational monitoring of the Earth’s vegetation(Qu et al.,2018;Yan et al.,2008).However,soil background dramatically affects the performances ofⅥs(Baret and Guyot,1991;Gilabert et al.,2002;Huete,1988;Qi et al,1994).

Effect of Maturity Indices on Growth and Quality of High Value Vegetables

The study took place at Bangladesh Agricultural Research Institute’s Olericulture Division’s research farm from March 2021 to February 2022 (BARI). In a protected net house, we investigated the impact of five different types of vegetables on various maturation stages, including tomato, broccoli, sweet pepper, cucumber, and netted melon. Vegetables cultivated under protected conditions in a transparent poly-film net house can improve quality, maturity, fruit size, and yield. When fruits and vegetables are picked before they are fully mature, they may stay green for longer, but they may not ripen to a satisfactory color and flavor, resulting in a loss of consumer confidence. Furthermore, because fruit continues to grow until the harvest, immature fruit will be smaller than mature fruit, reducing harvest yield. We tried to determine the right maturation stages in order to avoid product loss during our investigation. The tomato was found to be an appropriate size (6.5 cm length and 6.2 cm diameter), weight (84 g), TSS (4.5 percent), pH (4.3), “turning red”, and “tasty” at the week 5 stage, while the broccoli was found to be an appropriate size (12.0 cm length and 13.0 cm diameter), weight (360 g), and “green” color at the week 5 stage. At the week 6 stage, the nettled melon was found to be of appropriate size (15.2 cm length and 14.5 cm diameter), weight (800 g), TSS (10.8 percent), pH (6.3), “net fully developed” on the fruit skin and “much tasty,” while cucumber was found to be of appropriate size (8.8 - 10.8 cm length and 2.2 - 2.9 cm diameter), weight (61 - 88 g), TSS (3.8 - 4.1 percent), pH (6.3), “less powdery”. As a result, establishing the optimal maturity of our research will benefit both consumers and growers.

Advanced land observing satellite data to identify ground vegetation in a juniper forest,northeast Iran

Juniperus excelsa subsp.polycarpos,(Persian juniper),is found in northeast Iran.In this study,the relationship between ground cover and vegetation indices have been investigated using remote sensing data for a Persian juniper forest.Multispectral data were analyzed based on the Advanced Visible and Near Infrared Radiometer type 2 and panchromatic data obtained by the Panchromatic Remote-sensing Instrument for Stereo Mapping sensors,both on board the advanced land observing satellite(ALOS).The ground cover was calculated using field survey data from 25 sub-sample plots and the vegetation indices were derived with 595 maximum filtering algorithm from ALOS data.R2 values were calculated for the normalized difference vegetation index(NDVI)and various soil-adjusted vegetation indices(SAVI)with soilbrightness-dependent correction factors equal to 1 and 0.5,a modified SAVI(MSAVI)and an optimized SAVI(OSAVI).R2 values for the NDVI,MSAVI,OSAVI,SAVI(1),and SAVI(0.5)were 0.566,0.545,0.619,0.603,and 0.607,respectively.Total ratio vegetation index for arid and semi-arid regions based on spectral wavelengths of ALOS data with an R2 value 0.633 was considered.Results of the current study will be useful for forest inventories in arid and semi-arid regions in addition to assisting decisionmaking for natural resource managers.

Viscosity Indices of Rapeseed Oil

In this article, viscosity indices was presented for a number of vegetable oils, crude rapeseed oil, degummed rapessed oil, rapeseed oil dry, rapeseed oil bleache and refined rapeseed oil using two methods. Viscosity indices were calculated from the measured viscosity at 40℃ and 100℃ using ASTM D （American Society for Testing and Materials ） 2270 and method graphically using ASTM D 341. The viscosity-temperature coefficients for vegetable oils were calculated from the measured viscosity at 40℃ and 100℃.

Vegetation Change Prediction with Geo-Information Techniques in the Three Gorges Area of China

A computerized parametric methodology was applied to monitor, map, and estimate vegetation change in combination with '3S' (RS-remote sensing, GIS-geographic information systems, and GPS-global positioning system) technology and change detection techniques at a 1:50000 mapping scale in the Letianxi Watershed of western Hubei Province, China. Satellite images (Landsat TM 1997 and Landsat ETM 2002) and thematic maps were used to provide comprehensive views of surface conditions such as vegetation cover and land use change. With ER Mapper and ERDAS software, the normalized difference vegetation index (NDVI) was computed and then classified into six vegetation density classes. ARC/INFO and ArcView software were used along with field observation data by GPS for analysis. Results obtained using spatial analysis methods showed that NDVI was a valuable first cut indicator for vegetation and land use systems. A regression analysis revealed that NDVI explained 94.5% of the variations for vegetation cover in the largest vegetation area, indicating that the relationship between vegetation and NDVI was not a simple linear process. Vegetation cover increased in four of areas. This meant 60.9% of land area had very slight to slight vegetation change, while 39.1% had moderate to severe vegetation change. Thus, the study area, in general, was exposed to a high risk of vegetation cover change.

Vegetation fractional coverage change in a typical oasis region in Tarim River Watershed based on remote sensing

Vegetation fractional coverage （VFC） is an important index to describe and evaluate the ecological system. The vegetation index is widely used to monitor vegetation coverage in the field of remote sensing （RS）. In this paper, the author conducted a case study of the delta oasis of Weigan and Kuqa rivers, which is a typical saline area in the Tarim River Watershed. The current study was based on the TM/ETM＋ images of 1989, 2001, and 2006, and supported by Geographic Information System （GIS） spatial analysis, vegetation index, and dimidiate pixel model. In addition, VBSl （vegetation, bare soil and shadow indices） suitable for TM/ETM＋ irrlages, constructed with FCD （forest canopy density） model principle and put forward by ITTO （International Tropical Timber Organization）, was used, and it was applied to estimate the VFC. The estimation accuracy was later prow^n to be up to 83.52%. Further, the study analyzed and appraised the changes in vegetation patterns and revealed a pattern of spatial change in the vegetation coverage of the study area by producing the map of VFC levels in the delta oasis. Forest, grassland, and farmland were the three main land-use types with high and extremely-high coverage, and they played an important role in maintaining the vegetation. The forest area determined the changes of the coverage area, whereas the other two land types affected the directions of change. Therefore, planting trees, protecting grasslands, reclaiming farmlands, and controlling unused lands should be included in a long-term program because of their importance in keeping regional vegetation coverage. Finally, the dynamic variation of VFC in the study area was evaluated according to the quantity and spatial distribution rendered by plant cover diigital images to deeply analyze the reason behind the variation.

Leaf area index retrieval based on canopy reflectance and vegetation index in eastern China

The aim of this paper is to investigate the feasibility of using Landsat TM data to retrieve leaf area index (LAI). To get a LAI retrieval model based ground reflectance and vegetation index, detailed field data were collected in the study area of eastern China, dominated by bamboo, tea plant and greengage. Plant canopy reflectance of Landsat TM wavelength bands has been inversed using software of 6S. LAI is an important ecological parameter. In this paper, atmospheric corrected Landsat TM imagery was utilized to calculate different vegetation indices (VI), such as simple ratio vegetation index (SR), shortwave infrared modified simple ratio (MSR), and normalized difference vegetation index (NDVI). Data of 53 samples of LAI were measured by LAI-2000 (LI-COR) in the study area. LAI was modeled based on different reflectances of bands and different vegetation indices from Landsat TM and LAI samples data. There are certainly correlations between LAI and the reflectance of TM3, TM4, TM5 and TM7. The best model through analyzing the results is LAI = 1.2097*MSR + 0.4741 using the method of regression analysis. The result shows that the correlation coefficient R2 is 0.5157, and average accuracy is 85.75%. However, whether the model of this paper is suitable for application in subtropics needs to be verified in the future.

Estimation of vegetation biophysical parameters by remote sensing using radial basis function neural network

Hyperspectral reflectance (350~2500 nm) data were recorded at two different sites of rice in two experiment fields including two cultivars, and three levels of nitrogen (N) application. Twenty-five Vegetation Indices (VIs) were used to predict the rice agronomic parameters including Leaf Area Index (LAI, m2 green leaf/m2 soil) and Green Leaf Chlorophyll Density (GLCD, mg chlorophyll/m2 soil) by the traditional regression models and Radial Basis Function Neural Network (RBF). RBF emerged as a variant of Artificial Neural Networks (ANNs) in the late 1980’s. A large variety of training algorithms has been tested for training RBF networks. In this study, Original RBF (ORBF), Gradient Descent RBF (GDRBF), and Generalized Regression Neural Network (GRNN) were employed. Results showed that green waveband Normalized Difference Vegetation Index (NDVIgreen) and TCARI/OSAVI have the best prediction power for LAI by exponent model and ORBF respectively, and that TCARI/OSAVI has the best prediction power for GLCD by exponent model and GDRBF. The best performances of RBF are compared with the traditional models, showing that the relationship between VIs and agronomic variables are further improved when RBF is used. Compared with the best traditional models, ORBF using TCARI/OSAVI improves the prediction power for LAI by lowering the Root Mean Square Error (RMSE) for 0.1119, and GDRBF using TCARI/OSAVI improves the prediction power for GLCD by lowering the RMSE for 26.7853. It is concluded that RBF provides a useful exploratory and predictive tool when applied to the sensitive VIs.

Estimating vertical vegetation density through a SPOT5 imagery at multiple radiometric correction levels

There is growing concern about remote sensing of vertical vegetation density in rapidly expanding peri-urban interfaces. A widely used parameter for such density, i.e., leaf area index （LAI）, was measured in situ in Nanjing, China and then correlated with two vegetation indices （VI） derived from multiple radiometric correction levels of a SPOT5 imagery. The VIs were a normal- ized difference vegetation index （NDVI） and a ratio vegetation index （RVI）, while the four radiometric correction levels were i） post atmospheric correction reflectance （PAC）, ii） top of atmosphere reflectance （TOA）, iii） satellite radiance （SR） and iv） digital number （DN）. A total of 157 LAI-VI relationship models were established. The results showed that LA! is positively correlated with VI （r varies from 0.303 to 0.927, p 〈 0.001）. The R： values of＂pure＂ vegetation were generally higher than those of mixed vegetation. The average R2 values of about 40 models based on DN data （0.688） were higher than that of the routinely used PAC （0.648）. Independent variables of the optimal models for different vegetation quadrats included two vegetation indices at three radiometric correction lev- els, indicating the potential of vegetation indices at multiple radiometric correction levels in LAI inversion. The study demonstrates that taking heterogeneities of vegetation structures and uncertainties of radiometric corrections into account may help full mining of valuable information from remote sensing images, thus improving accuracies of LAI estimation.

Empirical Analysis of the Vegetable Industry in Hebei Province

We first introduce the status quo of the development of vegetable industry in Hebei Province,and then conduct empirical analysis of the development of vegetable industry in Hebei Province.Further,we analyze the development advantage of the vegetable industry in Hebei Province using SAI(Scale Advantage Indices) and SCA(Symmetric Comparative Advantage),drawing the conclusion that the vegetable industry in Hebei Province has much room for development;at the same time,we analyze the factors influencing vegetable consumption of residents in Hebei Province through the regression model,drawing the conclusion that the vegetable consumer price index is the main factor affecting the consumption.Finally we make recommendations for the development of vegetable industry in Hebei Province as follows:increasing financial input,promoting policy guarantee capacity;implementing brand strategy,promoting the competitiveness of products;improving the ecological environment,promoting industrialization of pollution-free vegetables.

Riparian Vegetation Dynamics Across Two Different Landscapes Along the River Cauvery in the Kodagu Region of Western Ghats

Decades of commercial planting and other anthropogenic processes are posing a threat to the riparian landscapes of the Cauvery river basin, which supports a high floral diversity. Despite this, the habitats in the upstream sections of the River Cauvery are still intact, as they are located in sacred groves. To understand the dynamism of riparian forests exposed to anthropogenic pressures, the upstream stretch of Cauvery extending from Kushalanagara to Talacauvery （~102 km） was categorized into two landscapes： agro ecosystem and sacred （i.e. preserved）. The tree species were sampled using belt transects at 5 km intervals and the regeneration status of endemic species assessed using quadrats. A total of 128 species belonging to 47 families, and representing 1,590 individuals, was observed. Amongst them, 65% of unique species were exclusive to sacred landscapes. A rarefaction plot confirmed higher species richness for the sacred compared to the agro ecosystem landscapes, and diversity indices with more evenness in distribution were evident in sacred landscapes. A significant loss of endemic tree species in the agro ecosystem landscapes was found. Overall, this study demonstrates that an intense biotic pressure in terms of plantations and other anthropogenic activities have altered the species composition of the riparian zone in non-sacred areas. A permanent policy implication is required for the conservation of riparian buffers to avoid further ecosystem degradation and loss of biodiversity.