Research on characteristics of biomass distribution in urban forests of Shanghai metropolis based on remote sensing and spatial analysis北大核心CSCD

Aims Monitoring and quantifying the biomass and its distribution in urban trees and forests are crucial to understanding the role of vegetation in an urban environment. In this paper, an estimation method for biomass of urban forests was developed for the Shanghai metropolis, China, based on spatial analysis and a wide variety of data from field inventory and remote sensing. Methods An optimal regression model between forest biomass and auxiliary variables was established by stepwise regression analysis. The residual value of regression model was computed for each of the sites sampled and interpolated by Inverse-distance weighting (IDW) to predict residual errors of other sites not subjected to sampling. Forest biomass in the study area was estimated by combining the regression model based on remote sensing image data and residual errors of spatial distribution map. According to the distribution of plantations and management practices, a total of 93 sample plots were established between June 2011 and June 2012 in the Shanghai metropolis. To determine a suitable model, several spectral vegetation indices relating to forest biomass and structure such as normalized difference vegetation index (NDVI), ratio vegetation index (RVI), difference vegetation index (DVI), soil-adjusted vegetation index (SAVI), and modified soil-adjusted vegetation index (MSAVI), and new images synthesized through band combinations such as the sum of TM2, TM3 and TM4 (denoted Band 234), and the sum of TM3, TM4 and TM5 (denoted Band 345) were used as alternative auxiliary parameters . Important findings The biomass density in urban forests of the Shanghai metropolis varied from 15 to 120 t•hm2. The higher densities of forest biomass concentrated mostly in the urban areas, e.g. in districts of Jing'an and Huangpu, mostly ranging from 35 to 70 t•hm2. Suburban localities such as the districts of Jiading and Qingpu had lower biomass densities at around 15 to 50 t•hm2. The biomass density of Cinnamomum camphora trees across the Shanghai metropolis varied between 20 and 110 t•hm2. The spatial biomass distribution of urban forests displayed a tendency of higher densities in northeastern areas and lower densities in southwestern areas. The total biomass was 3.57 million tons (Tg) for urban forests and 1.33 Tg for C. camphora trees. The overall forest biomass was also found to be distributed mostly in the suburban areas with a fraction of 93.9%, whereas the urban areas shared a fraction of only 6.1%. In terms of the areas, the suburban and urban forests accounted for 95.44% and 4.56%, respectively, of the total areas in the Shanghai metropolis. Among all the administrative districts, the Chongming county and the new district of Pudong had the highest and the second highest biomass, accounting for 20.1% and 19.18% of the total forest biomass, respectively. In contrast, the Jing'an district accounted for only 0.11% of the total forest biomass. The root-mean-square error (RMSE), mean absolute error (MAE) and mean relative error (MRE) of the model for estimating urban forest biomass in this study were 8.39, 6.86 and 24.22%, respectively, decreasing by 57.69%, 55.43% and 64.00% compared to the original simple regression model and by 62.21%, 58.50%, 65.40% compared to the spatial analysis method. Our results indicated that a more efficient way to estimate urban forest biomass in the Shanghai metropolis might be achieved by combining spatial analysis with regression analysis. In fact, the estimated results based on the proposed model are also more comparable to the up-scaled forest inventory data at a city scale than the results obtained using regression analysis or spatial analysis alone.     

Estimating Stem Volume Using QuickBird Imagery and Allometric Relationships for Open Populus xiaohei Plantations

There has been a great deal of interest in studying the crown of trees using remote sensing data. In this study, crown width was extracted from high-resolution QuickBird images for open Populus xiaohei plantations. Regression models for predicting the individual stem volumes of Populus xiaohei were established using extracted crown width, as well as estimated tree parameters （i.e. diameter at breast height [DBH] and tree height） as predictors. Our results indicated that crown width could be accurately extracted from QuickBird images using a multi-scale segmentation approach with a mean relative error of 5.74%, especially for wide-spacing stands. Using either extracted crown width alone or with estimated DBH and tree height can successfully estimate individual stem volume of Populus xiaohei with the R2 value ranging from 0.87 to 0.92 depending on different model forms. In particular, the two second-order polynomial models （model 2 and model 6）, based on QuickBird image-derived crown widths and estimated DBH and tree heights, respectively, were the best at describing the relationship between stem volume and tree characteristics.     

Elevational changes in vascular plants richness,diversity, and distribution pattern in Abune Yosef mountain range,Northern Ethiopia

The aim of this research is to investigate the patterns of vascular plant species richness,diversity,and distribution along an elevation gradient in the Abune Yosef mountain range,Ethiopia.Preferential systematic sampling was employed to collect vegetation and environmental data along the elevation gradient.We found that plant species richness declines monotonically from low to high elevations.Specifically,vascular plant species richness and diversity were lower in the Afroalpine grassland(high elevation)than in the Dry evergreen Afromontane forest and Ericaceous forest(low elevations).In contrast,endemic vascular plant richness was significantly higher in the Afroalpine grassland than in the Dry evergreen Afromontane forest and Ericaceous forest.Elevation showed a significant impact on the richness,diversity,and endemism of vascular plants.According to Sorensen's coefficient,the similarity between Dry evergreen Afromontane forest and Ericaceous forest vegetation types is higher(32%)than the similarity between Ericaceous forest and Afroalpine grassland(18%).Only 5%similarity was recorded between the Dry evergreen Afromontane forest and Afroalpine grassland.Growth forms showed different elevationai richness patterns.Trees and liana increased monotonically up to 3300 m.Shrub and herb richness patterns followed a hump-shaped and inverted hump-shaped pattern along the elevation gradient.The elevation patterns of vascular plant species richness,diversity,and growth form in the present study may be attributed to differences in management intensity,spatial heterogeneity,microclimatic variations,and anthropogenic disturbances.     

Estimating Stem Volume Using QuickBird Imagery and Allometric Relationships for Open Populus xiaohei Plantations

There has been a great deal of interest in studying the crown of trees using remote sensing data.In this study,crownwidth was extracted from high-resolution QuickBird images for open Populus xiaohei plantations.Regression modelsfor predicting the individual stem volumes of Populus xiaohei were established using extracted crown width,as well asestimated tree parameters(i.e.diameter at breast height[DBH]and tree height)as predictors.Our results indicated thatcrown width could be accurately extracted from QuickBird images using a multi-scale segmentation approach with a meanrelative error of 5.74%,especially for wide-spacing stands.Using either extracted crown width alone or with estimatedDBH and tree height can successfully estimate individual stem volume of Populus xiaohei with the R~2 value ranging from0.87 to 0.92 depending on different model forms.In particular,the two second-order polynomial models(model2 andmodel 6),based on QuickBird image-derived crown widths and estimated DBH and tree heights,respectively,were the bestat describing the relationship between stem volume and tree characteristics.     

Identification of the spectral characteristics of submerged plant Vallisneria spiralis

The relationship between land features and their spectral characteristics is important for the interpretation of remote sensing images. In this study, the spectral characteristics of a submerged plant Vallisneria spiralis with varied coverage was measured with a ground sensor/radiometer, FieldSpec? Pro JR Spectroradiometer in the laboratory and in the constructed wetland of “Mengqingyuan”, Shanghai, China. The results showed that the reflectance rate of Vallisneria spiralis rose with its increasing coverage, which was exhibited both at the visible band (500–650 nm) and the near infrared band (700–900 nm). Water quality influenced the reflectance rate with the primary differences between the laboratory and field experiments mainly occurring at the near-infrared band (700–900 nm). A regression analysis was carried out respectively between the coverage of Vallisneria spiralis and the reflectance rate at the wavelengths of Quick Bird 4 bands where the coverage responded to the strongest. These results of regression analyses showed a clear linear relationship, by which the coverage of Vallisneria spiralis could be quantitatively deduced from the reflectance rate measured in situ. The implications in terms of the ability of hyperspectral remote sensing to distinguish and monitor the distribution and dynamics of submerged vegetation on a large scale are discussed.     

Application of ecological-niche factor analysis in habitat assessment of giant pandas

Ecological-niche factor analysis (ENFA) is a multivariate approach to study geographic distribution of species on a large scale with only “presence” data. It has been widely applied in many fields including wildlife management, habitat assessment and habitat prediction. In this paper, this approach was applied in habitat suitability assessment for giant pandas in Pingwu County, Sichuan Province, China. With “presence” data of giant pandas and remote sensing data, habitat suitability of pandas in this county was evaluated based on ENFA model, and spatial distribution pattern of nature reserves and conservation gaps were then evaluated. The results show that giant pandas in this county prefer high-elevation zones (> 2128 m) dominated by coniferous forest, and mixed coniferous and deciduous broadleaf forest, and avoid deciduous broadleaf forest and shrubs. Pandas avoid staying at habitats with human disturbances. Farmland is a major factor threatening panda habitat. Panda habitat is mainly distributed in north and west of Pingwu with a total area of 234033 hm², 106345hm² for suitable habitat and 127688 hm² for marginally suitable habitat). 3 nature reserves were located in Pingwu, covering over 49.2% of total suitable habitat and 45.6% of total marginally suitable habitat. Although 47.2% of panda habitat was in reserves under protection, connectivity between reserves was weak and a conservation gap existed in the north part of Pingwu. Thus, a new nature reserve in Baima and Mupi should be established to link the isolated habitats.     

Soil Organic Matter Dynamics Along a Vertical Vegetation Gradient in the Gongga Mountain on the Tibetan Plateau

Our knowledge about soil organic matter (SOM) dynamics is limited although this is an important issue in the study of responses of ecosystems to global climate changes. Twelve sampling plots were set up every 200 m from 1 700 to 3 900 m along the vertical vegetation gradient along the east slope of Gongga Mountain. Samples were taken from all 12 plots for SOM content measurement, although only 5 of the 12 plots were subjected to radiocarbon measurements. A radiocarbon isotope method and a time-dependent model were used to quantify the SOM dynamics and SOM turnover rates along the vertical vegetation gradient. The results showed that the SOM turnover rate decreased and turnover time increased with soil depth for all vegetation types. The litter layer turnover rates presented a clear trend along the gradient. The litter layer turnover rates decreased with an increase in elevation, except that the litter layer turnover rate of mixed forest was higher than that of evergreen forest. Climatic factors, such as temperature and precipitation, were the main factors influencing the surface soil carbon dynamics. The turnover rates of the subsoil (including the A, B, and C horizons in the soil profiles) along the vertical gradient had no clear trends. The SOM of subalpine shrub and meadow turned over more slowly than that of the forest types in almost all soil horizons. The characteristic of short roots distributing in the upper part of the soil profile leads to different SOM dynamics of shrub and meadow compared with the forest types. Coniferous and mixed forests were susceptible to carbon loss from the young carbon pool, but their long and big roots resulted in high △^14C values of the deep soil profiles and increased the input of young carbon to the deep soil. In evergreen forest, the carbon cumulative ability from the B horizon to the C horizon was weak. The different vegetation types, together with their different modes of nutrient and carbon intake, may be the mechanism conditioning the subsoil organic matter dynamics.     

Toward a general evaluation model for soil respiration (GEMSR)

Soil respiration is an important component of terrestrial carbon budget. Its accurate evaluation is es- sential to the study of terrestrial carbon source/sink. Studies on soil respiration at present mostly focus on the temporal variations and the controlling factors of soil respiration, but its spatial variations and controlling factors draw less attention. Moreover, the evaluation models for soil respiration at present include only the effects of water and heat factors, while the biological and soil factors controlling soil respiration and their interactions with water and heat factors have not been considered yet. These models are not able to accurately evaluate soil respiration in different vegetation/terrestrial ecosystems at different temporal and spatial scales. Thus, a general evaluation model for soil respiration (GEMSR) including the interacting meteorological (water and heat factors), soil nutrient and biological factors is suggested in this paper, and the basic procedure developing GEMSR and the research tasks of soil respiration in the future are also discussed.     

Relationship between photochemical reflectance index with multi-angle hyper-spectrum and light use efficiency in urban green-land ecosystems北大核心CSCD

Aims: Light-use efficiency (LUE) is one of critical parameters in the terrestrial ecosystem production studies. Accurate determination of LUE is very important for LUE models to simulate gross primary productivity (GPP) at regional and global scales. We used eddy covariance technique measurement and tower-based, multi-angular spectro-radiometer observations in autumn 2012 to explore the relationship between bidirectional reflectance distribution function (BRDF) corrected photochemical reflectance index (PRI) and LUE in different phenology and environment conditions in urban green-land ecosystems. Methods: Using the eddy covariance technique, we estimated the temporal changes in GPP during the autumn 2012 over Beijing Olympic Forest Park. LUE was calculated as the ratio of GPP to the difference between incoming photosynthetically active radiation (PA R) and PA R reflected from the canopy. Daily PRI values were averaged from the BRDF using semi-empirical kernel driven models. The absolute greenness index (2G-RB) was made by webcam at a constant view zenith and view azimuth angle at solar noon. The logistic function was used to fit the time series of the greenness index. The onset of phonological stages was defined as the point when the curvature reached its maximum value. Important findings: Webcamera-observed greenness index (2G-RB) showed a decreasing trend. There was a highly significant relationship between 2G-RB and air temperature (R2 = 0.60, p < 0.001). This demonstrates that air temperature is the main driving factor to determine the phenology. PRI estimated from multi-angle hyper-spectrum can estimate LUE in urban green-land ecosystems in vigorous photosynthetic period. The correlation was the strongest (R2 = 0.70, p < 0.001) in the peak photosynthetic period. PRI relates better to LUE under high temperature (>15°C) with high vapour pressure deficit (VPD) (>700 Pa) and high PAR (>300 μmol·m-2·s-1). The LUE was up-scaled to landscape/regional scales based on these relationships and phenology. It can also be used for the estimation of GPP of urban green-land with high accuracy.     

Vegetation Mapping of the Mond Protected Area of Bushehr Province （South-west Iran）

Arid regions of the world occupy up to 35% of the earth＇s surface, the basis of various definitions of climatic conditions, vegetation types or potential for food production. Due to their high ecological value, monitoring of arid regions is necessary and modern vegetation studies can help in the conservation and management of these areas. The use of remote sensing for mapping of desert vegetation is difficult due to mixing of the spectral reflectance of bright desert soils with the weak spectral response of sparse vegetation. We studied the vegetation types in the semiarid to arid region of Mond Protected Area, south-west Iran, based on unsupervised classification of the Spot XS bands and then produced updated maps. Sixteen map units covering 12 vegetation types were recognized in the area based on both field works and satellite mapping. Halocnemum strobilaceum and Suaeda fruticosa vegetation types were the dominant types and Ephedra foliata, Salicornia europaea-Suaeda heterophylla vegetation types were the smallest. Vegetation coverage decreased sharply with the increase in salinity towards the coastal areas of the Persian Gulf. The highest vegetation coverage belonged to the riparian vegetation along the Mond River, which represents the northern boundary of the protected area. The location of vegetation types was studied on the separate soil and habitat diversity maps of the study area, which helped in final refinements of the vegetation map produced.     

Evaluation of different models to describe egg and pupal development of Xyleborus fornicatus Eichh, （Coleoptera： Scolytidae）, the shot-hole borer of tea in Sri Lanka

Development data of eggs and pupae ofXyleborusfornicatus Eichh. （Coleoptera： Scolytidae）, the shot-hole borer of tea in Sri Lanka, at constant temperatures were used to evaluate a linear and seven nonlinear models for insect development. Model evaluation was based on fit to data （residual sum of squares and coefficient of determination or coefficient of nonlinear regression）, number of measurable parameters, the biological value of the fitted coefficients and accuracy in the estimation of thresholds. Of the nonlinear models, the Lactin model fitted experimental data well and along with the linear model, can be used to describe the temperature-dependent development of this species.     

浙江省亚热带常绿阔叶林群落分类中的数学手段

 Though it is efficient using mathematical approch to the classification of plant communities, yet the general mathematical means does not satisfy the needs of community classification of subtropical evergreen broad-leaf forest. In this paper the author discussed the effectiveness and deficiency of the ’Importance Value’ in naming and classifying the evergreen broad-leaf forest and suggested to adopt the ’Index of Similarity of Spatz (1970)’ combining with the ’Importance Value’ as the basis for classifying the subtropical evergreen broad-leaf forest.     

Bayesian state-space modeling of metapopulation dynamics in the Glanville fritillary butterfly

The complexity of mathematical models of ecological dynamics varies greatly, and it is often difficult to judge what would be the optimal level of complexity in a particular case. Here we compare the parameter estimates, model fits, and predictive abilities of two models of metapopulation dynamics: a detailed individual-based model (IBM) and a population-based stochastic patch occupancy model (SPOM) derived from the IBM. The two models were fitted to a 17-year time series of data for the Glanville fritillary butterfly (Melitaea cinxia) inhabiting a network of 72 small meadows. The data consisted of biannual counts of larval groups (IBM) and the annual presence or absence of local populations (SPOM). The models were fitted using a Bayesian state-space approach with a hierarchical random effect structure to account for observational, demographic, and environmental stochasticities. The detection probability of larval groups (IBM) and the probability of false zeros of local populations (SPOM) in the observation models were simultaneously estimated from the time-series data and independent control data. Prior distributions for dispersal parameters were obtained from a separate analysis of mark–recapture data. Both models fitted the data about equally, but the results were more precise for the IBM than for the SPOM. The two models yielded similar estimates for a random effect parameter describing habitat quality in each patch, which were correlated with independent empirical measures of habitat quality. The modeling results showed that variation in habitat quality influenced patch occupancy more through the effects on movement behavior at patch edges than on carrying capacity, whereas the latter influenced the mean population size in occupied patches. The IBM and the SPOM explained 63% and 45%, respectively, of the observed variation in the fraction of occupied habitat area among 75 independent patch networks not used in parameter estimation. We conclude that, while carefully constructed, detailed models can have better predictive ability than simple models, this advantage comes with the cost of greatly increased data requirements and computational challenges. Our results illustrate how complex models can be helpful in facilitating the construction of effective simpler models.     

Comparison of Vegetation Indices and Red-edge Parameters for Estimating Grassland Cover from Canopy Reflectance Data

There has been a great deal of Interests in the estimation of grassland biophysical parameters such as percentage of vegetation cover （PVC）, aboveground biomass, and leaf-area index with remote sensing data at the canopy scale. In this paper, the percentage of vegetation cover was estimated from vegetation indices using Moderate Resolution Imaging Spectroradiometer （MODIS） data and red-edge parameters through the first derivative spectrum from in situ hypserspectral reflectance data. Hyperspectral reflectance measurements were made on grasslands in Inner Mongolia, China, using an Analytical Spectral Devices spectroradiometer. Vegetation indices such as the difference, simple ratio, normalized difference, renormalized difference, soil-adjusted and modified soil-adjusted vegetation indices （DVI, RVI, NDVI, RDVI, SAVI L=0.5 end MSAVI2） were calculated from the hyperspectral reflectance of various vegetation covers. The percentage of vegetation cover was estimated using an unsupervised spectral-contextual classifier automatically. Relationships between percentage of vegetation cover and various vegetation indices and red-edge parameters were compared using a linear and second-order polynomial regression. Our analysis indicated that MSAVI2 and RVI yielded more accurate estimations for a wide range of vegetation cover than other vegetation indices and red-edge parameters for the linear and second-order polynomial regression, respectively.     

Composition and distribution of ground-dwelling beetles among oak fragments and surrounding pine plantations in a temperate forest of North China

In this study, we compared ground-dwelling beetle assemblages （Coleoptera） from a range of different oak fragments and surrounding conifer plantations to evaluate effects of forest size and surrounding matrix habitat in a temperate forest of north China. During 2000, beetles were sampled via pitfall traps within two large oak fragments （ca. 2.0-4.0 ha）, two small oak fragments （ca. 0.2-0.4 ha） and two surrounding matrices dom- inated by pine plantations （〉4 ha） in two sites of different aspects. Overall, no significantly negative effects from forest patch size and the surrounding matrix habitat were detected in total species number and abundance of ground-dwelling beetles. However, compared with small oak patches or pine plantations, more species were associated with an affinity for at least one large oak patch of the two aspects. Multivariate regression trees showed that the habitat type better determined the beetle assemblage structure than patch size and aspect, indicating a strong impact of the surrounding matrix. Linear mixed models indicated that species richness and abundance of all ground-dwelling beetles or beetle families showed different responses to the selected environmental variables. Our results suggest that more disturbed sites are significantly poorer in oak forest specialists, which are usually more abundant in large oak fragments and decrease in abundance or disappear in small fragments and surrounding matrix habitats. Thus, it is necessary to preserve a minimum size of forest patch to create conditions characteristic for forest interior, rather than the more difficult task of increasing habitat connectivity.     

Carbon cycle of larch plantation based on CO2FIX model北大核心CSCD

Aims Plantations play important roles in modifying regional carbon budget and maintaining regional carbon balance. In this study, we assessed larch plantation (Larix gmelinii var. principis-rupprechtii) carbon dynamics in Weichang County from a perspective of the forest biomass-soil-wood-products chain. Our objectives were to elucidate the carbon sink capacity of larch plantation and the influences of biomass, soil and wood product pools on carbon balance. Methods CO2FIX model was used to evaluate the carbon storage and flow of larch plantation over a time span of 120 years. Input data for model were derived from practical investigations and published papers. We validated the simulated results and found that this model was suitable in the region and the simulated results were reliable. Important findings (1) Soil was the largest carbon pool for larch plantation and the wood product pool had the smallest carbon storage. Meanwhile, carbon storage in wood products gradually increased with time. (2) In a rotation of 50 years from secondary poplar-birch forest to larch plantation, 250 t C•hm2 was sequestrated by the larch plantation. 70% of the carbon was transferred into soil in the form of litter and logging slash and the other 30% was transferred into wood products. (3) Larch plantation was a carbon sink during most of its growing period and turned to temporary carbon source when it was harvested. Larch plantation could sequestrate about 0.3 t C•hm2•a1 in the long term. Our results indicated the importance of wood product carbon pool in carbon dynamics of plantation, which facilitated our understanding in the carbon dynamics and capacity of plantation.     

Remote sensing and geographic information systems in the spatial temporal dynamics modeling of infectious diseases

Similar to species immigration or exotic species invasion,infectious disease transmis-sion is strengthened due to the globalization of human activities. Using schistosomiasis as an exam-ple,we propose a conceptual model simulating the spatio-temporal dynamics of infectious diseases. We base the model on the knowledge of the interrelationship among the source,media,and the hosts of the disease. With the endemics data of schistosomiasis in Xichang,China,we demonstrate that the conceptual model is feasible; we introduce how remote sensing and geographic information systems techniques can be used in support of spatio-temporal modeling; we compare the different effects caused to the entire population when selecting different groups of people for schistosomiasis control. Our work illustrates the importance of such a modeling tool in supporting spatial decisions. Our mod-eling method can be directly applied to such infectious diseases as the plague,lyme disease,and hemorrhagic fever with renal syndrome. The application of remote sensing and geographic informa-tion systems can shed light on the modeling of other infectious disease and invasive species studies.     

Evaluation of satellite data efficiency in identification of plant groups

Application of remote sensing (RS) and geographical information system (GIS) techniques has been increased in natural sciences. In fact, it is inevitable applying of these techniques in vegetation studies due to the existence of some problems in traditional methods (e.g. sampling, calculation, analysis and so on). On this scope, scientists must have sufficient information about the efficiency of these techniques as a useful tool in their studies. This study aims to evaluate the IRS-P6 LISS III and Landsat ETM⁺ efficiency in plant groups’ identification. In order to this purpose, 143 training samples were collected from areas that showed homogenous composition of plant species in at least area of 3600 m² (60 × 60 m). Coordinates of these training samples were recorded using a GPS device and transferred to a GIS database. Also, ENVI 4.2 package has used to process and analyze the satellites data. Several methods of processing such as; spectral separability, supervised classification and assessment of classification accuracy were used in order to gain a satisfy evaluation of the data efficiency. The results indicated that net farming of alfalfa and Juniperus polycarpus–Artemisia kopetdaghensisi community have the most separability on the satellite images (1.99 for Landsat and 2 for IRS). Against, the least separabilities on the Landsat data were between Ju. polycarpus–Onobrychis cornuta and Ju. polycarpus–Ar. kopetdaghensis communities (1.57) and between Ju. polycarpus–Ar. kopetdaghensis and Ju. polycarpus–Agropyron intermedium (1.53) on the IRS data. According to these results, it is concluded that the satellite data are somedeal able to identify plant groups when vegetation communities are sufficiently homogenous, abundant and spectrally and ecologically separable.     

Evaluation of satellite data efficiency in identification of plant groups

Application of remote sensing (RS) and geographical information system (GIS) techniques has been increased in natural sciences. In fact, it is inevitable applying of these techniques in vegetation studies due to the existence of some problems in traditional methods (e.g. sampling, calculation, analysis and so on). On this scope, scientists must have sufficient information about the efficiency of these techniques as a useful tool in their studies. This study aims to evaluate the IRS-P6 LISS III and Landsat ETM⁺ efficiency in plant groups’ identification. In order to this purpose, 143 training samples were collected from areas that showed homogenous composition of plant species in at least area of 3600 m² (60 × 60 m). Coordinates of these training samples were recorded using a GPS device and transferred to a GIS database. Also, ENVI 4.2 package has used to process and analyze the satellites data. Several methods of processing such as; spectral separability, supervised classification and assessment of classification accuracy were used in order to gain a satisfy evaluation of the data efficiency. The results indicated that net farming of alfalfa and Juniperus polycarpus–Artemisia kopetdaghensisi community have the most separability on the satellite images (1.99 for Landsat and 2 for IRS). Against, the least separabilities on the Landsat data were between Ju. polycarpus–Onobrychis cornuta and Ju. polycarpus–Ar. kopetdaghensis communities (1.57) and between Ju. polycarpus–Ar. kopetdaghensis and Ju. polycarpus–Agropyron intermedium (1.53) on the IRS data. According to these results, it is concluded that the satellite data are somedeal able to identify plant groups when vegetation communities are sufficiently homogenous, abundant and spectrally and ecologically separable.