Remote sensing of subtropical tree diversity:The underappreciated roles of the practical definition of forest canopy and phenological variation

Tree species diversity is vital for maintaining ecosystem functions,yet our ability to map the distribution of tree diversity is limited due to difficulties in traditional field-based approaches.Recent developments in spaceborne remote sensing provide unprecedented opportunities to map and monitor tree diversity more efficiently.Here we built partial least squares regression models using the multispectral surface reflectance acquired by Sentinel-2 satellites and the inventory data from 74 subtropical forest plots to predict canopy tree diversity in a national natural reserve in eastern China.In particular,we evaluated the underappreciated roles of the practical definition of forest canopy and phenological variation in predicting tree diversity by testing three different definitions of canopy trees and comparing models built using satellite imagery of different seasons.Our best models explained 42%–63%variations in observed diversities in cross-validation tests,with higher explanation power for diversity indices that are more sensitive to abundant species.The models built using imageries from early spring and late autumn showed consistently better fits than those built using data from other seasons,highlighting the significant role of transitional phenology in remotely sensing plant diversity.Our results suggested that the cumulative diameter(60%–80%)of the biggest trees is a better way to define the canopy layer than using the subjective fixeddiameter-threshold(5–12 cm)or the cumulative basal area(90%–95%)of the biggest trees.Remarkably,these approaches resulted in contrasting diversity maps that call attention to canopy structure in remote sensing of tree diversity.This study demonstrates the potential of mapping and monitoring tree diversity using the Sentinal-2 data in species-rich forests.

Mapping winter wheat using phenological feature of peak before winter on the North China Plain based on time-series MODIS data

By employing the unique phenological feature of winter wheat extracted from peak before winter （PBW） and the advantages of moderate resolution imaging spectroradiometer （MODIS） data with high temporal resolution and intermediate spatial resolution, a remote sensing-based model for mapping winter wheat on the North China Plain was built through integration with Landsat images and land-use data. First, a phenological window, PBW was drawn from time-series MODIS data. Next, feature extraction was performed for the PBW to reduce feature dimension and enhance its information. Finally, a regression model was built to model the relationship of the phenological feature and the sample data. The amount of information of the PBW was evaluated and compared with that of the main peak （MP）. The relative precision of the mapping reached up to 92% in comparison to the Landsat sample data, and ranged between 87 and 96% in comparison to the statistical data. These results were sufficient to satisfy the accuracy requirements for winter wheat mapping at a large scale. Moreover, the proposed method has the ability to obtain the distribution information for winter wheat in an earlier period than previous studies. This study could throw light on the monitoring of winter wheat in China by using unique phenological feature of winter wheat.

Should phenological information be applied to predict agronomic traits across growth stages of winter wheat?

Most existing agronomic trait models of winter wheat vary across growing seasons, and it is an open question whether a unified statistical model can be developed to predict agronomic traits using a vegetation index(VI) across multiple growing seasons. In this study, we constructed a hierarchical linear model(HLM) to automatically adapt the relationship between VIs and agronomic traits across growing seasons and tested the model’s performance by sensitivity analysis. Results demonstrated that(1) optical VIs give poor performance in predicting AGB and PNC across all growth stages, whereas VIs perform well for LAI, LGB, LNC, and SPAD.(2) The sensitivity indices of the phenological information in the AGB and PNC prediction models were 0.81–0.86 and 0.66–0.73, whereas LAI, LGB, LNC, and SPAD prediction models produced sensitivity indexes of 0.01–0.02, 0.01–0.02, 0.01–0.02, and 0.02–0.08, respectively.(3) The AGB and PNC prediction models considering ZS were more accurate than the prediction models based on VI. Whether or not phenological information is used, there was no difference in model accuracy for LGB,LNC, SPAD, and LAI. This study may provide a guideline for deciding whether phenological correction is required for estimation of agronomic traits across multiple growing seasons.

Detecting winter canola(Brassica napus) phenological stages using an improved shape-model method based on time-series UAV spectral data

Accurate information about phenological stages is essential for canola field management practices such as irrigation, fertilization, and harvesting. Previous studies in canola phenology monitoring focused mainly on the flowering stage, using its apparent structure features and colors. Additional phenological stages have been largely overlooked. The objective of this study was to improve a shape-model method(SMM) for extracting winter canola phenological stages from time-series top-of-canopy reflectance images collected by an unmanned aerial vehicle(UAV). The transformation equation of the SMM was refined to account for the multi-peak features of the temporal dynamics of three vegetation indices(VIs)(NDVI, EVI, and CI). An experiment with various seeding scenarios was conducted, including four different seeding dates and three seeding densities. Three mathematical functions: asymmetric Gaussian function(AGF), Fourier function, and double logistic function, were employed to fit timeseries vegetation indices to extract information about phenological stages. The refined SMM effectively estimated the phenological stages of canola, with a minimum root mean square error(RMSE) of 3.7 days for all phenological stages. The AGF function provided the best fitting performance, as it captured multiple peaks in the growth dynamics characteristics for all seeding date scenarios using four scaling parameters. For the three selected VIs, CIred-edgeachieved the greatest accuracy in estimating the phenological stage dates. This study demonstrates the high potential of the refined SMM for estimating winter canola phenology.

Effect of various combinations of temperature during different phenological periods on indica rice yield and quality in the Yangtze River Basin in China

Rice grain yield and quality declines are due to unsuitable temperatures from wide regions and various sowing dates.This study aimed to evaluate the effects of temperature on rice yield and quality at different phenological periods and obtain suitable temperatures for phenological periods in the Yangtze River Basin,China.This study conducted experiments on different sowing dates under different areas in the Yangtze River Basin to observe and compare the differences in rice growth,yield,and quality,controlling for regional varieties.The results showed significant differences in rice growth,yield,and quality among sowing dates and areas,which were related to the average daily temperature during the vegetative period(VT)and the first 20 days of the grain-filling period(GT20).In addition,there was a smaller variation in the average daily temperature in the reproductive period(RT)than in the two phenological periods.Therefore,according to the VT and GT20 thresholds of different yields and qualities,the experimental results were divided into four scenarios(Ⅰ,Ⅱ,Ⅲ,andⅣ)in this study.In Scenario I,high head rice production(rice grain yield multiplied by head rice rate)and rice quality could be obtained.The head rice production of ScenariosⅢandⅣwas lower than that of ScenarioⅠ,by 30.1 and 27.6%,respectively.In Scenario II,the head rice production increased insignificantly while the chalky grain rate and chalkiness were 50.6 and 56.3%higher than those of Scenario I.In conclusion,the Scenario I combination with VT ranges of 22.8-23.9℃and GT20 ranges of 24.2-27.0℃or the combination with VT ranges of 23.9-25.3℃and GT20 ranges of 24.2-24.9℃,which can be obtained by adjusting sowing date and selecting rice varieties with suitable growth periods,is recommended to achieve high levels of rice grain yield and quality in the Yangtze River Basin.

Grapevine Phenological Quantitative Trait SSR Genotyping Using High-Throughput HRM-PCR Analysis

Discrimination among grapevine varieties based on quantitative traits,such as flowering,veraison and ripening dates is crucial for variety selection in the context of climate change and in breeding programs.These traits are under complex genetic control for which 6 linked SSR loci(VVS2,VVIn16,VMC7G3,VrZAG29,VMC5G7,and VVIB23)have been identified.Using these markers in HRM-PCR analysis,we assessed genetic diversity among a large collection of 192 grapevine varieties.The grapevine germplasm used encompasses the majority of Greek vineyard with 181 varieties,3 prominent foreign varieties and 11 varieties of Palestinian origin.The SSR markers used were highly polymorphic,displaying unique melting curves for unusually higher number of samples than generally observed in SSR analysis.This prompted us to examine sequence composition for selected samples and found that variation present as SNPs in the flanking sequences of SSR motifs was responsible for the observed polymorphism.Hence,HRM-PCR proved to be a tool of higher analytical power to distinguish genotypes surpassing the discrimination power of conventional gel-based SSR analysis.The study provides a better understanding of genetic variation of SSR marker loci associated to phenological traits in grapevine varieties,signifying an analytical methodology that may be of higher discrimination power in detection of polymorphism for utilization in breeding programs.

Exploration of Genetic Pattern of Phenological Traits in Wheat (Triticum aestivum L.) under Drought Stress

Drought is the major detrimental environmental factor for wheat(Triticum aestivum L.)production.The exploration of genetic patterns underlying drought tolerance is of great significance.Here we report the gene actions controlling the phenological traits using the line×tester model studying 27 crosses and 12 parents under normal irrigation and drought conditions.The results interpreted via multiple analysis(mean performance,correlations,principal component,genetic analysis,heterotic and heterobeltiotic potential)disclosed highly significant differences among germplasm.The phenological waxiness traits(glume,boom,and sheath)were strongly interlinked.Flag leaf area exhibits a positive association with peduncle and spike length under drought.The growing degree days(heat-units)greatly influence spikelets and grains per spike,however,the grain yield/plant was significantly reduced(17.44 g to 13.25 g)under drought.The principal components based on eigenvalue indicated significant PCs(first-seven)accounted for 79.9%and 73.9%of total variability under normal irrigation and drought,respectively.The investigated yield traits showed complex genetic behaviour.The genetic advance confronted a moderate to high heritability for spikelets/spike and grain yield/plant.The traits conditioned by dominant genetic effects in normal irrigation were inversely controlled by additive genetic effects under drought and vice versa.The magnitude of dominance effects for phenological and yield traits,i.e.,leaf twist,auricle hairiness,grain yield/plant,spikelets,and grains/spike suggests that selection by the pedigree method is appropriate for improving these traits under normal irrigation conditions and could serve as an indirect selection index for improving yield-oriented traits in wheat populations for drought tolerance.However,the phenotypic selection could be more than effective for traits conditioned by additive genetic effects under drought.We suggest five significant cross combinations based on heterotic and heterobeltiotic potential of wheat genotypes for improved yield and enhanced biological production of wheat in advanced generations under drought.

Morphological Characterization and Phenological Modeling of <i>Jatropha platyphylla</i>(Euphorbiaceae) Muell. Arg. Genotypes

Morphological characterization and phenological modeling were carried out on genotypes of Jatropha platyphylla collected from the states of Sinaloa and Durango, Mexico. The morphological characterization evidenced the existence of monoecious plants, finding individuals with male and female flowers in the same inflorescence. Fruit with four seeds was also found. The phenological study was divided into two phases and calculated in thermal requirement (°D): Vegetative [seedtime (0), germination (24), emergence (98), cotyledons (87), second (302) and fourth (524) true leaves, end of vegetative growth (302)] and reproductive [flowering (303), fructification (342), maturation (126), defoliation and senescence (450)]. The thermal constant (2558) was similar in all eight genotypes. The phenological stages and the accumulated degree days were adjusted with a third-degree polynomial (Stage = -0.0041x3 + 0.7446x2 - 8.6808x + 6.2448) (R2 = 0.99%) stage. The development of phenological models facilitates the prediction of the flowering date for the selection of varieties with high oil and protein content.

The Phenological Stages of Apple Tree in the North Eastern of Iran

The main objective of this study was to evaluate the process of parameters such as mean temperature;total precipitation on phenology and phenological stages of apple golden type in Razavi Khorasan. For this reason, long-term data of absolute minimum daily temperature, precipitation, humidity, as well as Digital Elevation Model (DEM) was used. After collecting data on phenology and Growing Degree Days (GDD) for golden apple, to pass each phenological stage at different growth stages, the start and end dates, phenological stages of the locations were identified. Then, regression equations with variable longitude, latitude and altitude on SPSS software at level of 50% and 95%, respectively were used, and finally phenological stages and spatial distribution maps of temperature and precipitation variables based on these equations were drawn in ARC GIS software. The analysis of the phenological stages showed that Torbate Heydarieh station has a decreasing trend which is significant at 1% in all stages of phenology and Ghoochan station does not show any significant increase or decrease trend at all stages of phenology.

Effects of temperature on flowering phenological traits of Populus euphratica Oliv. and Populus pruinosa Schrenk populations, Xinjiang, China

The aims of this study were to explore the interspecific differences of Populus euphratica Oliv.and Populus pruinosa Schrenk populations and the intraspecific differences of males and females within the same species in flowering phenological traits,and the effects of temperatures on flowering phenological traits in different growth years(2001–2003 and 2013–2015).The results showed that P.euphratica population flowered earlier than P.pruinosa population.Moreover,flowering phenological period of population,number of days of flowering phenological period per population,number of days of flowering phenological period per plant and average number of days of flowering period per plant of P.euphratica population were less than those of P.pruinosa population.The differences between male and female within the same species indicated that the flowering periods of males P.euphratica and P.pruinosa populations were earlier than those of female plants.For both species,flowering phenological traits were significantly and negatively correlated with the average temperatures in previous ten days,previous one month and previous three months of flowering.Both species are sensitive to temperature changes and adjust to the changes by advancing the start of flowering and prolonging the duration of flowering.

Seasonal Responses of Net Primary Productivity of Vegetation to Phenological Dynamics in the Loess Plateau, China

With global warming, the great changes in the patterns of plant growth have occurred. The conditions in early spring and late autumn have changed the process of vegetation photosynthesis, which are expected to have a significant impact on net primary productivity(NPP) and affect the global carbon cycle. Currently, the seasonal response characteristics of NPP to phenological changes in dryland ecosystems are still not well defined. This article calibrated and analyzed the normalized difference vegetation index(NDVI)time series of Advanced Very-High-Resolution Radiometer(AVHRR) data from 1982 to 2015 in the Loess Plateau, China. The spatial and temporal distributions of vegetation phenology and NPP in the Loess Plateau under semihumid and semiarid conditions were investigated. The seasonal variation in the NPP response to vegetation phenology under the climate change was also analyzed. The results showed that, different from the northern forest, there was distinct spatial heterogeneity in the effect of climate change on the dynamic change in vegetation growth in the Loess Plateau: 1) an advance of the start of the growing season(SOS) and a delay of the end of the growing season(EOS) significantly increased the NPP in spring and autumn, respectively, in the humid southeast;2) in the arid northwest, the NPP did not significantly increase in spring and autumn but significantly decreased in summer.

Monitoring of winter wheat distribution and phenological phases based on MODIS time-series： A case study in the Yellow River Delta, China

Accurate winter wheat identification and phenology extraction are essential for field management and agricultural policy making. Here, we present mechanisms of winter wheat discrimination and phenological detection in the Yellow River Delta（YRD） region using moderate resolution imaging spectroradiometer（MODIS） time-series data. The normalized difference vegetation index（NDVI） was obtained by calculating the surface reflectance in red and infrared. We used the Savitzky-Golay filter to smooth time series NDVI curves. We adopted a two-step classification to identify winter wheat. The first step was designed to mask out non-vegetation classes, and the second step aimed to identify winter wheat from other vegetation based on its phenological features. We used the double Gaussian model and the maximum curvature method to extract phenology. Due to the characteristics of the time-series profiles for winter wheat, a double Gaussian function method was selected to fit the temporal profile. A maximum curvature method was performed to extract phenological phases. Phenological phases such as the green-up, heading and harvesting phases were detected when the NDVI curvature exhibited local maximum values. The extracted phenological dates then were validated with records of the ground observations. The spatial patterns of phenological phases were investigated. This study concluded that, for winter wheat, the accuracy of classification is 87.07%, and the accuracy of planting acreage is 90.09%. The phenological result was comparable to the ground observation at the municipal level. The average green-up date for the whole region occurred on March 5, the average heading date occurred on May 9, and the average harvesting date occurred on June 5. The spatial distribution of the phenology for winter wheat showed a significant gradual delay from the southwest to the northeast. This study demonstrates the effectiveness of our proposed method for winter wheat classification and phenology detection.

Forest phenological patterns of Northeast China inferred from MODIS data

The role of remote sensing in phenological studies is increasingly regarded as a key to understand large area seasonal phenomena. This paper describes the application of Moderate Resolution Imaging Spectroradiometer (MODIS) time series data for forest phenological patterns. The forest phenological phase of Northeast China (NE China) and its spatial characteristics were inferred using 1-km 10-day MODIS normalized difference vegetation index (NDVI) datasets of 2002. The threshold-based method was used to estimate three key forest phenological variables, which are the start of growing season (SOS), the end of growing season (EOS) and growing season length (GSL). Then the spatial patterns of forest phenological variables of NE China were mapped and analyzed. The derived phenological variables were validated by the field observed data from published papers in the same study area. Results indicate that forest phenological phase from MODIS data is comparable with the observed data. As the derived forest phenological pattern is related to forest type distribution, it is helpful to discriminate between forest types.

Resources of Colored-leaf Trees and Their Phenological Ornamental Characteristics in Lhasa City

By using biological five points,expert consultation and fuzzy comprehensive evaluation,the species,phenological ornamental characteristics,and landscape application of colored-leaf trees in Lhasa City were surveyed systematically. The results show that there are 42 species of colored-leaf trees in Lhasa City,belonging to 31 genera and 18 families,of which there are 5,19,and 18 species of spring,autumn,and common colored-leaf trees,accounting for 11.90%,45.24% and 42.86% of total number of the investigated tree species respectively. It is clearly seen that there are too few species of spring colored-leaf trees in the city. There are 19 and 17 species of trees possessing red and yellow leaves,while the leaves of other 6 species of colored-leaf trees are shown in other colors,and the number of their species accounts for 45.24%,40.48% and 14.28% of total number of the investigated tree species respectively. The best ornamental periods of spring,autumn and common colored-leaf trees in Lhasa City average 38.4,41.8 and 251.8 d respectively. Prunus cerasifera Ehrhar f. atropurpurea Rehd.,Ulmus pumila L.,Populus × beijingensis W. Y. Hsu,and Salix alba are applied most frequently in Lhasa City,and their relative frequency is 18.67%,10.29%,9.91% and 8.95% respectively. According to the comprehensive assessment value of their ornamental characteristics,the ornamental characteristics of 15 species of colored-leaf trees in Lhasa City are good or very good,and there is a positive correlation between the comprehensive assessment value and relative frequency. Based on the investigation,the current application situation and problems of colored-leaf trees in Lhasa City were analyzed,and solutions to the problems were proposed.

Plant diversity and phenological pattern in the montane wet temperate forests of the southern Western Ghats,India

Plants diversity and phenological pattern of the trees were monitored in a montane wet temperate forest （shola） in the Kukkal Forest, Palni hills of the southern Western Ghats, India. Twelve random plots were selected for sampling the vegetation. For phenological studies, twenty-three fleshy fruit trees were identified in the study area and 10 individuals of each species were selected to record the phenological events fortnightly between April 2002 and April 2004. The phenological events were divided into vegetative and reproductive phases. A total of 2279 individuals were inventoried which belong to 83 species, 68 genera and 40 families. About 30% of the species were endemic to the Western Ghats. The most dominant species （〉 1 cm dbh） was Psychotria nilgiriensis var. astephana （Rubiaceae）, which accounted for 12% of the total sampled individuals. Lauraceae was the dominant family accounting for 20% of the individuals. Fruiting peak occurred in July 2003 and least in June 2002. During the peak period, fruits of 85 individuals of six species were observed. Syzygium tamilnadensis, Ilex wightiana and Beilschmiedia wightii fruited only once during the two years of study. The number of fruiting species showed no correlation with rainfall （r = 0.26, p = 0.2）, while a correlation was found with fruit abundance （r = 0.40, p 〈 0.05）. The results indicate that the montane wet temperate forest is unique in their diversity and a conspicuous display in phenology.

Remote Sensing Derived Phenological Metrics to Assess the Spatio-Temporal Growth Variability in Cropping Fields

Precision Agriculture (PA) recognizes and manages intra-field spatial variability to increase profitability and reduced environmental impact. Site Specific Crop Management (SSCM), a form of PA, subdivides a cropping field into uniformly manageable zones, based on quantitative measurement of yield limiting factors. In Mediterranean environments, the spatial and temporal yield variability of rain-fed cropping system is strongly influenced by the spatial variability of Plant Available Water-holding Capacity (PAWC) and its strong interaction with temporally variable seasonal rainfall. The successful adoption of SSCM depends on the understanding of both spatial and temporal variabilities in cropping fields. Remote sensing phenological metrics provide information about the biophysical growth conditions of crops across fields. In this paper, we examine the potential of phenological metrics to assess the spatial and temporal crop yield variability across a wheat cropping field at Minnipa, South Australia. The Minnipa field was classified into three management zones using prolonged observations including soil assessment and multiple year yield data. The main analytical steps followed in this study were: calculation of the phenological metrics using time series NDVI data from Moderate Resolution Imaging Spectroscope (MODIS) for 15 years (2001-2015);producing spatial trend and temporal variability maps of phenological metrics;and finally, assessment of association between the spatial patterns and temporal variability of the metrics with management zones of the cropping field. The spatial trend of the seasonal peak NDVI metric showed significant association with the management zone pattern. In terms of temporal variability, Time-integrated NDVI (TINDVI) showed higher variability in the “good” zone compared with the “poor” zone. This indicates that the magnitude of the seasonal peak is more sensitive to soil related factors across the field, whereas TINDVI is more sensitive to seasonal variability. The interpretation of the association between phenological metrics and the management zone site conditions was discussed in relation to soil-climate interaction. The results demonstrate the potential of the phenological metrics to assess the spatial and temporal variability across cropping fields and to understand the soil-climate interaction. The approach presented in this paper provides a pathway to utilize phenological metrics for precision agricultural management application.

Occurrence of Carabid Beetles in the Phenological Stages of Weedy Plants

In the current study, we investigated the relationship between the abundance of carabid species (Coleoptera: Carabidae) and the phenological stages of weedy plants growing in edge habitats. A survey of carabid beetles was conducted in edge habitats between forest fragments and soybean/corn crops or orange orchards in five sites located in northeastern S?o Paulo state, Brazil, from November 2005 to May 2008. Beetles were captured with pitfall traps, and multiple regression analysis was used to determine the carabid species that prevailed on each of the phenological stages of weeds. In total, 1115 individuals, representing 26 genera and 52 species, were captured. Selenophorus species, mainly Selenophorus seriatoporus Putzeys, Selenophorus sp.4, Helluomorphoides squiresi (Chaudoir), Tetragonoderus laevigatus Chaudoir, Athrostictus sp.1 were abundant during the reproductive phenological stages of weedy plants, which suggests that these carabid species might prefer to feed on the seeds of weedy plants.

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.

Phenological Complementarity Does not Enhance Ecosystem Production in Undisturbed Steppe Community

Communities with more species could have a greater variety of species＇ characteristics, leading to more effective use of limiting resources through niche partitioning （complementarity） and therefore greater production. The effect of phenologlcal complementarity （PC） on ecosystem production has not been fully Investigated. The seasonal responses of all vascular plant species were tracked to test the effect of phenologlcal complementarity on ecosystem production within a natural stable steppe community. Although a significant phenologlcal pattern was observed, PC had no significant correlation with community production. The value of PC varied with years, but was observed only In a relatively narrow range during the experimental period. Species diversity （richness and evenness） had no correlation with the ecosystem production. The results suggest that the effect of PC may be saturated and has no contribution to the improvement of ecosystem production In a stable natural grassland community with abundant species.

Mapping paddy rice in Jiangsu Province,China,based on phenological parameters and a decision tree model

Timely and accurate mapping of rice planting areas is crucial under China's current cropping structure. This study proposes a new paddy rice mapping method by combining phenological parameters and a decision tree model.Six phenological parameters were developed to identify paddy rice areas based on the analysis of the Moderate Resolution Imaging Spectroradiometer (MODIS)Enhanced Vegetation Index (EVI)time series and the Land Surface Water Index (LSWI)time series.The six phenological parameters considered the performance of different land cover types during specific phenological phases (EVI1 and EVI2),one-half of or the entire rice growing cycle (LSWI1 and LSWI2),and the shape of the LSWI time series (KurtosisLSWI and SkewnessLSWI).A hierarchical decision tree model was designed to classify paddy rice areas according to the potential separability of different land cover types in paired phenological parameter spaces.Results showed that the decision tree model was more sensitive to LSWI1,LSWI2,and SkewnessLswi than the other phenological parameters.A paddy rice map of Jiangsu Province for 2015 was generated with an optimal threshold set of(0.4,0.42,9,19,1.5,-1.7,0.0)with a total accuracy of 93.9%.The MODIS-derived paddy rice map generally agreed with the paddy land fraction map from the National Land Cover Dataset project,but there were regional discrepancies because of their different definitions of land use and the inability of MODIS to map paddy rice at a fragmental level.The MODIS-derived paddy rice map showed high correlation (R^2=0.85)with county-level agricultural statistics.The results of this study indicate that the phenological parameter-based paddy rice mapping algorithm could be applied at larger spatial scales.