环境卫星CCD影像在太湖沉水植物监测中的应用

利用环境一号卫星 CCD影像，综合现场巡视情况，对2014年1—5月太湖梅梁湖水域的沉水植被区域进行分析与研究，分别取沉水植物、水华、地表植被与水体4个样本区域，对它们的光谱反射率曲线进行分析，得到沉水植物光谱反射率曲线相比其他样本区域独特的结论。并根据此特征，结合基于提取样本运行决策树的方法，以2014年5月1日为例，提取出了太湖梅梁湖水域沉水植物的分布区域与面积。     

Comparative studies on accumulation of Cr from metal solution and tannery effluent under repeated metal exposure by aquatic plants: its toxic effects

The present study demonstrates comparison of Cr accumulatingpotential by the plants of Najas indica Cham. (submerged),Vallisneria spiralis L. (rooted submerged) and Alternanthera sessilis R. Br. (rooted emergent) under repeatedmetal exposure and its effect on chlorophyll and protein concentrations. These plants were treated with different concentrations of Cr under repeated exposure in controlled laboratory conditions to assess the maximum metal accumulationpotential. The plants of V. spiralis accumulated significantly high amount of Cr under laboratory conditions incomparison to N. indica and A. sessilis. The maximumaccumulation of 1378, 458 and 201 g g^-1 dw Cr was found in the leaves of V. spiralis, N. indica and A. sessilis, respectively at 8 mg L^-1 after 9 day of Cr exposure. These plants have shown a decrease in chlorophyll andprotein concentrations with increase in Cr concentrations. In view of high accumulation of Cr in V. spiralis, the plantswere treated with different concentrations of tannery effluent collected from Common Effluent Treatment Plant, Unnao (UP). Theplants of V. spiralis treated with 100% tannery wastewatershowed the maximum accumulation (57.5 g g^-1 dw) of Cr in the roots after 10 days of exposure. The plants were foundeffective in removing Cr from solution and tannery effluent.     

Estimation of Vegetation Coverage in Semi-arid Sandy Land Based on Multivariate Statistical Modeling Using Remote Sensing Data

The estimation of vegetation coverage is essential in the monitoring and management of arid and semi-arid sandy lands. But how to estimate vegetation coverage and monitor the environmental change at global and regional scales still remains to be further studied. Here, combined with field vegetation survey, multispectral remote sensing data were used to estimate coverage based on theoretical statistical modeling. First, the remote sensing data were processed and several groups of spectral variables were selected/proposed and calculated, and then statistically correlated to measured vegetation coverage. Both the single- and multiple-variable-based models were established and further analyzed. Among all single-variable-based models, that is based on Normalized Difference Vegetation Index showed the highest R (0.900) and R ² (0.810) as well as lowest standard estimate error (0.128024). Since the multiple-variable-based model using multiple stepwise regression analysis behaved much better, it was determined as the optimal model for local coverage estimation. Finally, the estimation was conducted based on the optimal model and the result was cross-validated. The coefficient of determination used for validation was 0.867 with a root-mean-squared error (RMSE) of 0.101. The large-scale estimation of vegetation coverage using statistical modeling based on remote sensing data can be helpful for the monitoring and controlling of desertification in arid and semi-arid regions. It could serve for regional ecological management which is of great significance.     

Yield and leaf area index estimations for sunflower plants using unmanned aerial vehicle images

Vegetation is commonly monitored to improve efficiency of various agricultural practices. Spatial and temporal changes in plant growth and development can be monitored with the aid of remote sensing techniques employing ground, aerial, and satellite platforms. Unmanned aerial vehicles (UAV) and multi-spectral cameras developed for UAVs have an important potential for agricultural management activities with high-resolution spatial and temporal images. However, UAV images should be assessed based on ground measurements for using these images as a decision-support tool in agriculture. This study was conducted to estimate sunflower leaf area index (LAI) and yield with the aid of Normalized Difference Vegetation Index (NDVI) images generated from raw UAV images. Furthermore, UAV-based NDVI values were compared with NDVI values calculated by using hyper-spectral measurements carried out with a ground-based spectroradiometer. Between July and August of 2017, six flight missions were conducted and spectral measurements were made simultaneously. A significant correlation (R²?=?0.77) was determined between NDVI values that belong to UAV platform and spectroradiometer. Also, regression models developed for sunflower LAI and yield estimation depending UAV-based NDVI have R² values of 0.88 and 0.91, respectively.     

A Multivariate Analysis of Biophysical Parameters of Tallgrass Prairie Among Land Management Practices and Years

Six treatments of eastern Kansas tallgrass prairie – native prairie, hayed, mowed, grazed, burned and untreated – were studied to examine the biophysical effects of land management practices on grasslands. On each treatment, measurements of plant biomass, leaf area index, plant cover, leaf moisture and soil moisture were collected. In addition, measurements were taken of the Normalized Difference VegetationIndex (NDVI), which is derived from spectral reflectance measurements. Measurements were taken in mid-June, mid-July and late summer of 1990 and 1991. Multivariate analysis of variance was used to determine whether there were differences in the set of variables among treatments and years. Follow-up tests included univariate t-tests to determine whichvariables were contributing to any significant difference. Results showed a significant difference (p < 0.0005) among treatments in the composite of parameters during each of the months sampled. In most treatment types, there was asignificant difference between years within each month. The univariate tests showed, however, that only some variables, primarily soil moisture, were contributing to this difference. We conclude that biomass and % plant cover show the best potential to serve as long-term indicators of grassland condition as they generally were sensitive to effects ofdifferent land management practices but not to yearlychange in weather conditions. NDVI was insensitive to precipitation differences between years in July for most treatments, but was not in the native prairie. Choice of sampling time is important for these parameters to serve effectively as indicators.     

Fucus spiralis as monitoring tool of metal contamination in the northwest coast of Portugal under the European Water Framework Directives

Metals (Cd, Cr, Cu, Fe, Mn and Zn) in coastal seawaters and soft tissues of macroalga Fucus spiralis from the northwest coast of Portugal were determined to assess spatial variations of metal bioavailabilities and bioaccumulation factors to compare different ecological quality classifications. Both coastal seawaters and soft tissues of F. spiralis showed significant spatial variations in their metal concentrations along the coast. The macroalgae F. spiralis accumulated more efficiently Cd, Mn and Zn and showed low bioaccumulation factors to Cr, Cu and Fe. Regarding the metal guidelines of the Norwegian Pollution Control Authority, the entire northwest (NW) coast of Portugal in April 2013 should be classified as ‘class I—unpolluted’ for all metals, except in Ave for Cu (‘class II—moderately polluted’) and Cavado for Cd and Cu (‘class II—moderately polluted’), revealing the low metal bioavailabilities of these seawaters. As there were always significant positive correlations between all metals in seawaters and F. spiralis, this macroalga species was considered a suitable monitoring tool of metal contamination in the NW coast of Portugal and a useful aquatic organism to be included in the European Environmental Specimen Banks in order to establish a real-time environmental monitoring network under the European Water Framework Directives.     

Monitoring and assessing of landscape heterogeneity at different scales

In numerous studies, spatial and spectral aggregations of pixel information using average values from imaging spectrometer data are suggested to derive spectral indices and the subsequent vegetation parameters that are derived from these. Currently, there are very few empirical studies that use hyperspectral data, to support the hypothesis for deriving land surface variables from different spectral and spatial scales. In the study at hand, for the first time ever, investigations were carried out on fundamental scaling issues using specific experimental test flights with a hyperspectral sensor to investigate how vegetation patterns change as an effect of (1) different spatial resolutions, (2) different spectral resolutions, (3) different spatial and spectral resolutions as well as (4) different spatial and spectral resolutions of originally recorded hyperspectral image data compared to spatial and spectral up- and downscaled image data. For these experiments, the hyperspectral sensor AISA-EAGLE/HAWK (DUAL) was mounted on an aircraft to collect spectral signatures over a very short time sequence of a particular day. In the first experiment, reflectance measurements were collected at three different spatial resolutions ranging from 1 to 3 m over a 2-h period in 1 day. In the second experiment, different spectral image data and different additional spatial data were collected over a 1-h period on a particular day from the same test area. The differently recorded hyperspectral data were then spatially and spectrally rescaled to synthesize different up- and down-rescaled images. The normalised difference vegetation index (NDVI) was determined from all image data. The NDVI heterogeneity of all images was compared based on methods of variography. The results showed that (a) the spatial NDVI patterns of up- and downscaled data do not correspond with the un-scaled image data, (b) only small differences were found between NDVI patterns determined from data recorded and resampled at different spectral resolutions and (c) the overall conclusion from the tests carried out is that the spatial resolution is more important in determining heterogeneity by means of NDVI than the depth of the spectral data. The implications behind these findings are that we need to exercise caution when interpreting and combining spatial structures and spectral indices derived from satellite images with differently recorded geometric resolutions.     

基于实测光谱的海河悬浮物浓度反演研究

悬浮物浓度是评价水质优劣的重要指标之一。以天津滨海新区海河为研究区域,进行光谱数据测量和水体样本采集,并在实验室进行水质参数提取,得到归一化光谱反射率和光谱一阶微分数据。然后对光谱测量数据和悬浮物浓度实测值进行相关性分析,发现896 nm归一化光谱反射率和光谱反射率比值(R_(896)/R_(546))与悬浮物浓度相关性较好。最后,分别建立单波段、波段比值和一阶微分函数拟合模型,进行对比分析。结果表明,基于R_(896)/R_(546)的二次多项式模型拟合效果最好,方差齐性检验(F)值也是最高的,可用于该地区水体的悬浮物浓度反演和预测。     

Vegetation NDVI Linked to Temperature and Precipitation in the Upper Catchments of Yellow River

Vegetation in the upper catchment of Yellow River is critical for the ecological stability of the whole watershed. The dominant vegetation cover types in this region are grassland and forest, which can strongly influence the eco-environmental status of the whole watershed. The normalized difference vegetation index (NDVI) for grassland and forest has been calculated and its daily correlation models were deduced by Moderate Resolution Imaging Spectroradiometer products on 12 dates in 2000, 2003, and 2006. The responses of the NDVI values with the inter-annual grassland and forest to three climatic indices (i.e., yearly precipitation and highest and lowest temperature) were analyzed showing that, except for the lowest temperature, the yearly precipitation and highest temperature had close correlations with the NDVI values of the two vegetation communities. The value of correlation coefficients ranged from 0.815 to 0.951 (p?<?0.01). Furthermore, the interactions of NDVI values of vegetation with the climatic indicators at monthly interval were analyzed. The NDVI of vegetation and three climatic indices had strong positive correlations (larger than 0.733, p?<?0.01). The monthly correlations also provided the threshold values for the three climatic indictors, to be used for simulating vegetation growth grassland under different climate features, which is essential for the assessment of the vegetation growth and for regional environmental management.     

Predicting field capacity,wilting point,and the other physical properties of soils using hyperspectral reflectance spectroscopy: two different statistical approaches

In this study, we examined the ability of reflectance spectroscopy to predict some of the most important soil parameters for irrigation such as field capacity (FC), wilting point (WP), clay, sand, and silt content. FC and WP were determined for 305 soil samples. In addition to these soil analyses, clay, silt, and sand contents of 145 soil samples were detected. Raw spectral reflectance (raw) of these soil samples, between 350 and 2,500-nm wavelengths, was measured. In addition, first order derivatives of the reflectance (first) were calculated. Two different statistical approaches were used in detecting soil properties from hyperspectral data. Models were evaluated using the correlation of coefficient (r), coefficient of determination (R ²), root mean square error (RMSE), and residual prediction deviation (RPD). In the first method, two appropriate wavelengths were selected for raw reflectance and first derivative separately for each soil property. Selection of wavelengths was carried out based on the highest positive and negative correlations between soil property and raw reflectance or first order derivatives. By means of detected wavelengths, new combinations for each soil property were calculated using rationing, differencing, normalized differencing, and multiple regression techniques. Of these techniques, multiple regression provided the best correlation (P?<?0.01) for selected wavelengths and all soil properties. To estimate FC, WP, clay, sand, and silt, multiple regression equations based on first(2,310)-first(2,360), first(2,310)-first(2,360), first(2,240)-first(1,320), first(2,240)-first(1,330), and raw(2,260)-raw(360) were used. Partial least square regression (PLSR) was performed as the second method. Raw reflectance was a better predictor of WP and FC, whereas first order derivative was a better predictor of clay, sand, and silt content. According to RPD values, statistically excellent predictions were obtained for FC (2.18), and estimations for WP (2.0), clay (1.8), and silt (1.63) were acceptable. However, sand values were poorly predicted (RDP?=?0.63). In conclusion, both of the methods examined here offer quick and inexpensive means of predicting soil properties using spectral reflectance data.     

Analysis of agricultural drought using vegetation temperature condition index (VTCI) from Terra/MODIS satellite data

The most commonly used normalized difference vegetation index (NDVI) from remote sensing often fall short in real-time drought monitoring due to a lagged vegetation response to drought. Therefore, research recently emphasized on the use of combination of surface temperature and NDVI which provides vegetation and moisture conditions simultaneously. Since drought stress effects on agriculture are closely linked to actual evapotranspiration, we used a vegetation temperature condition index (VTCI) which is more closely related to crop water status and holds a key place in real-time drought monitoring and assessment. In this study, NDVI and land surface temperature (T _s) from MODIS 8-day composite data during cloud-free period (September–October) were adopted to construct an NDVI–T _s space, from which the VTCI was computed. The crop moisture index (based on estimates of potential evapotranspiration and soil moisture depletion) was calculated to represent soil moisture stress on weekly basis for 20 weather monitoring stations. Correlation and regression analysis were attempted to relate VTCI with crop moisture status and crop performance. VTCI was found to accurately access the degree and spatial extent of drought stress in all years (2000, 2002, and 2004). The temporal variation of VTCI also provides drought pattern changes over space and time. Results showed significant and positive relations between CMI (crop moisture index) and VTCI observed particularly during prominent drought periods which proved VTCI as an ideal index to monitor terminal drought at regional scale. VTCI had significant positive relationship with yield but weakly related to crop anomalies. Duration of terminal drought stress derived from VTCI has a significant negative relationship with yields of major grain and oilseeds crops, particularly, groundnut.     

Hyperspectral Characteristics of Canopy Components and Structure for Phenological Assessment of an Invasive Weed

Spectral reflectance values of four canopy components (stems, buds, opening flowers, and postflowers of yellow starthistle (Centaurea solstitialis)) were measured to describe their spectral characteristics. We then physically combined these canopy components to simulate the flowering stage indicated by accumulated flower ratios (AFR) 10%, 40%, 70%, and 90%, respectively. Spectral dissimilarity and spectral angles were calculated to quantitatively identify spectral differences among canopy components and characteristic patterns of these flowering stages. This study demonstrated the ability of hyperspectral data to characterize canopy components, and identify different flowering stages. Stems had a typical spectral profile of green vegetation, which produced a spectral dissimilarity with three reproduction organs (buds, opening flowers, and postflowers). Quantitative differences between simulated flower stages depended on spectral regions and phenological stages examined. Using full-range canopy spectra, the initial flowering stage could be separated from the early peak, peak, and late flowering stages by three spectral regions, i.e. the blue absorption (around 480 nm) and red absorption (around 650 nm) regions and NIR plateau from 730 nm to 950 nm. For airborne CASI data, only the red absorption region and NIR plateau could be used to identify the flowering stages in the field. This study also revealed that the peak flowering stage was more easily recognized than any of the other three stages.     

Spatial–temporal dynamics of NDVI and Chl-a concentration from 1998 to 2009 in the East coastal zone of China: integrating terrestrial and oceanic components

Annual normalized difference vegetation index (NDVI) and chlorophyll-a (Chl-a) concentration are the most important large-scale indicators of terrestrial and oceanic ecosystem net primary productivity. In this paper, the Sea-viewing Wide Field-of-view Sensor level 3 standard mapped image annual products from 1998 to 2009 are used to study the spatial–temporal characters of terrestrial NDVI and oceanic Chl-a concentration on two sides of the coastline of China by using the methods of mean value (M), coefficient of variation (CV), the slope of unary linear regression model (Slope), and the Hurst index (H). In detail, we researched and analyzed the spatial–temporal dynamics, the longitudinal zonality and latitudinal zonality, the direction, intensity, and persistency of historical changes. The results showed that: (1) spatial patterns of M and CV between NDVI and Chl-a concentration from 1998 to 2009 were very different. The dynamic variation of terrestrial NDVI was much mild, while the variation of oceanic Chl-a concentration was relatively much larger; (2) distinct longitudinal zonality was found for Chl-a concentration and NDVI due to their hypersensitivity to the distance to shoreline, and strong latitudinal zonality existed for Chl-a concentration while terrestrial NDVI had a very weak latitudinal zonality; (3) overall, the NDVI showed a slight decreasing trend while the Chl-a concentration showed a significant increasing trend in the past 12 years, and both of them exhibit strong self-similarity and long-range dependence which indicates opposite future trends between land and ocean.     

A new multiscale approach for monitoring vegetation using remote sensing-based indicators in laboratory, field, and landscape

Remote sensing is an important tool for studying patterns in surface processes on different spatiotemporal scales. However, differences in the spatiospectral and temporal resolution of remote sensing data as well as sensor-specific surveying characteristics very often hinder comparative analyses and effective up- and downscaling analyses. This paper presents a new methodical framework for combining hyperspectral remote sensing data on different spatial and temporal scales. We demonstrate the potential of using the “One Sensor at Different Scales” (OSADIS) approach for the laboratory (plot), field (local), and landscape (regional) scales. By implementing the OSADIS approach, we are able (1) to develop suitable stress-controlled vegetation indices for selected variables such as the Leaf Area Index (LAI), chlorophyll, photosynthesis, water content, nutrient content, etc. over a whole vegetation period. Focused laboratory monitoring can help to document additive and counteractive factors and processes of the vegetation and to correctly interpret their spectral response; (2) to transfer the models obtained to the landscape level; (3) to record imaging hyperspectral information on different spatial scales, achieving a true comparison of the structure and process results; (4) to minimize existing errors from geometrical, spectral, and temporal effects due to sensor- and time-specific differences; and (5) to carry out a realistic top- and downscaling by determining scale-dependent correction factors and transfer functions. The first results of OSADIS experiments are provided by controlled whole vegetation experiments on barley under water stress on the plot scale to model LAI using the vegetation indices Normalized Difference Vegetation Index (NDVI) and green NDVI (GNDVI). The regression model ascertained from imaging hyperspectral AISA-EAGLE/HAWK (DUAL) data was used to model LAI. This was done by using the vegetation index GNDVI with an R ² of 0.83, which was transferred to airborne hyperspectral data on the local and regional scales. For this purpose, hyperspectral imagery was collected at three altitudes over a land cover gradient of 25 km within a timeframe of a few minutes, yielding a spatial resolution from 1 to 3 m. For all recorded spatial scales, both the LAI and the NDVI were determined. The spatial properties of LAI and NDVI of all recorded hyperspectral images were compared using semivariance metrics derived from the variogram. The first results show spatial differences in the heterogeneity of LAI and NDVI from 1 to 3 m with the recorded hyperspectral data. That means that differently recorded data on different scales might not sufficiently maintain the spatial properties of high spatial resolution hyperspectral images.     

基于卫星影像的太湖蓝藻水华遥感强度指数和等级划分算法设计

对太湖地区近10余年来共32景Landsat TM/ETM遥感影像进行大气校正处理,获得地表反射率影像,在这些影像上采集了分布在不同片区、不同发生季节、不同集聚程度的蓝藻水华样区,提取了不同蓝藻水华的可见一近红外波段反射率数据.统计表明蓝藻水华在TM 4波段的反射率有较宽的动态范围,能定量反映蓝藻集聚程度,TM 2也是...     

Comparison of different semi-empirical algorithms to estimate chlorophyll-a concentration in inland lake water

Based on in situ water sampling and field spectral measurement from June to September 2004 in Lake Chagan, a comparison of several existing semi-empirical algorithms to determine chlorophyll-a (Chl-a) content was made by applying them to the field spectra and in situ chlorophyll measurements. Results indicated that the first derivative of reflectance was well correlated with Chl-a. The highest correlation between the first derivative and Chl-a was at 680 nm. The two-band model, NIR/red ratio of R_710/670, was also an effective predictor of Chl-a concentration. Since the two-band ratios model is a special case of the three-band model developed recently, three-band model in Lake Chagan showed a higher resolution. The new algorithm named reverse continuum removal relies on the reflectance peak at 700 nm whose shape and position depend strongly upon chlorophyll concentration: The depth and area of the peak above a baseline showed a linear relationship to Chl-a concentration. All of the algorithms mentioned proved to be of value and can be used to predict Chl-a concentration. Best results were obtained by using the algorithms of the first derivative, which yielded R ² around 0.74 and RMSE around 6.39 μg/l. The two-band and three-band algorithms were further applied to MERIS when filed spectral were resampled with regard to their center wavelengths. Both algorithms showed an adequate precision, and the differences on the outcome were small with R ² = 0.70 and 0.71.     

Using NDVI to Assess Vegetative Land Cover Change in Central Puget Sound

We used the Normalized Difference Vegetation Index (NDVI) in the rapidly growing Puget Sound region over three 5-year time blocks between 1986–1999 at three spatial scales in 42 Watershed Administrative Units (WAUs) to assess changes in the amounts and patterns of green vegetation. On average, approximately 20% of the area in each WAU experienced significant NDVI change over each 5-year time block. Cumulative NDVI change over 15 years (summing change over each 5-year time block) was an average of approximately 60% of each WAU, but was as high as 100% in some. At the regional scale, seasonal weather patterns and green-up from logging were the primary drivers of observed increases in NDVI values. At the WAU scale, anthropogenic factors were important drivers of both positive and negative NDVI change. For example, population density was highly correlated with negative NDVI change over 15 years (r = 0.66, P < 0.01), as was road density (r = 0.71, P < 0.01). At the smallest scale (within 3 case study WAUs) land use differences such as preserving versus harvesting forest lands drove vegetation change. We conclude that large areas within most watersheds are continually and heavily impacted by the high levels of human use and development over short time periods. Our results indicate that varying patterns and processes can be detected at multiple scales using changes in NDVIa values.     

Applying Satellite Imagery to Triage Assessment of Ecosystem Health

Considerable evidence documents that certain changes in vegetation and soils result in irreversibly degraded rangeland ecosystems. We used Advanced Very High Resolution Radiometer (AVHRR) imagery to develop calibration patterns of change in the Normalized Difference Vegetation Index (NDVI) over the growing season for selected sites for which we had ground data and historical data characterizing these sites as irreversibly degraded. We used the NDVI curves for these training sites to classify and map the irreversibly degraded rangelands in southern New Mexico. We composited images into four year blocks: 1988–1991, 1989–1992, and 1990–1993. The overlap in pixels classified as irreversibly degraded ranged from 42.6% to 84.3% in year block comparisons. Quantitative data on vegetation composition and cover were collected at 13 sites within a small portion of the study area. Wide coverage reconnaissance of boundaries between vegetation types was also conducted for comparisons with year block maps. The year block 1988–1991 provided the most accurate delineation of degraded areas. The rangelands of southern New Mexico experienced above average precipitation from 1990–1993. The above average precipitation resulted in spatially variable productivity of ephemeral weedy plants on the training sites and degraded rangelands which resulted in much smaller areas classified as irreversibly degraded. We selected imagery for a single year, 1989, which was characterized by the absence of spring annual plant production in order to eliminate the confounding effect of reflectance from annual weeds. That image analysis classified more than 20% of the rangelands as irreversibly degraded because areas with shrub-grass mosaic were included in the degraded classification. The single year image included more than double the area classified as irreversibly degraded by the year blocks. AVHRR imagery can be used to make triage assessments of irreversibly degraded rangeland but such assessment requires understanding productivity patterns and variability across the landscapes of the region and careful selection of the years from which imagery is chosen.     

Impacts of ecological water conveyance on groundwater dynamics and vegetation recovery in the lower reaches of the Tarim River in northwest China

The ecological water conveyance project (EWCP) in the lower reaches of the Tarim River provided a valuable opportunity to study hydro-ecological processes of desert riparian vegetation. Ecological effects of the EWCP were assessed at large spatial and temporal scales based on 13 years of monitoring data. This study analyzed the trends in hydrological processes and the ecological effects of the EWCP. The EWCP resulted in increased groundwater storage—expressed as a general rise in the groundwater table—and improved soil moisture conditions. The change of water conditions also directly affected vegetative cover and the phenology of herbs, trees, and shrubs. Vegetative cover of herbs was most closely correlated to groundwater depth at the last year-end (R?=?0.81), and trees and shrubs were most closely correlated to annual average groundwater depth (R?=?0.79 and 0.66, respectively). The Normalized Difference Vegetation Index (NDVI) responded to groundwater depth on a 1-year time lag. Although the EWCP improved the NDVI, the study area is still sparsely vegetated. The main limitation of the EWCP is that it can only preserve the survival of existing vegetation, but it does not effectively promote the reproduction and regeneration of natural vegetation.     

基于RS和GIS技术的贵州省植被生态环境监测分析

为阐明贵州省植被生态环境变化的整体状况，基于RS和GIS技术，应用美国国家航空航天局最新的全球植被指数变化研究数据（GIMMS），通过计算月归一化植被指数（NDVI）变化率，并对研究区一元线性回归模拟，分析了贵州省1982年-2003年的地表植被覆盖。结果表明：22年来，研究区植被覆盖呈增加趋势，表明贵州省植被生态环境向好的方向发展；贵州省平均植被覆盖在春季和秋季呈上升趋势，夏季和冬季呈下降趋势，其中春季对植被覆盖总变化量的贡献最大；植被覆盖程度增减因区域不同而异，变化程度呈增加的区域主要位于贵,ki-I省的中部地区；变化程度呈减小的区域分布在贵州省的四周边缘。