Postfire stand structure in a semiarid savanna: cross-scale challenges estimating biomass.

Algorithms relating remotely sensed woody cover to biomass are often the basis for large-scale inventories of aboveground carbon stocks. However, these algorithms are commonly applied in a generic fashion without consideration of disturbances that might alter vegetation structure. We compared field and remote sensing estimates of woody biomass on savannas with contrasting disturbance (fire) histories and assessed potential errors in estimating woody biomass from cover without considering fire history. Field surveys quantified multilayer cover (MLC) of woody and succulent plants on sites experiencing wildfire in 1989 or 1994 and on nearby unburned (control) sites. Remote sensing estimates of the woody cover fraction (WCF) on burned and control sites were derived from contemporary (2005) dry-season Landsat Thematic Mapper imagery (during a period when herbaceous cover was senescent) using a probabilistic spectral mixture analysis model. Satellite WCF estimates were compared to field MLC assessments and related to aboveground biomass using allometry. Field-based MLC and remotely sensed WCFs both indicated that woody cover was comparable on control areas and areas burned 11-16 years ago. However, biomass was approximately twofold higher on control sites. Canopy cover was a strong predictor of woody biomass on burned and control areas, but fire history significantly altered the linear cover-biomass relationship on control plots to a curvilinear relationship on burned plots. Results suggest predictions of woody biomass from "generic" two-dimensional (2-D) cover algorithms may underestimate biomass in undisturbed stands and overestimate biomass in stands recovering from disturbance. Improving the accuracy of woody-biomass estimates from field and/or remotely sensed cover may therefore require disturbance-specific models or detection of vegetation height and transforming 2-D vegetation cover to 3-D vegetation volume.     

Optimizing model for land use/land cover retrieval from remote sensing imagery based on variable precision rough sets

The suitable spectral mode in remote sensing is often desirable to facilitate the inversion of ecological environment and landscape. This paper put forward an optimizing model based on variable precision rough sets (VPRS) for the land cover discrimination in wetland inventory. In the case study of Lake Baiyangdian which has important ecological functions to the northern China, this model is established successfully according to the domain-experts knowledge. The procedure is as follows. First step is data collection, including remote-sensing data (e.g., Landsat-5 TM bands), the digitized relief maps, and statistical yearbooks. Second, the remote sensing imagery (RSI) and relief maps are co-registered into the same resolution. Third, a condition set, including various attributes is derived from spectral bands, band math or ratio indices based on previous studies, at the same time, the decision set is derived from true land types after investigation and validation. Then, the remote sensing decision table (RSDT) is constructed by linking condition set with decision set according to the sequential pixels in RSI. Fourth, we create one forward greedy searching algorithm based on VPRS to handle this RSDT. After adjusting parameters such as β and knowledge granularity diameter (KGD), we obtain the stable optimized results. Comparative experiments and evaluation show that the discrimination or retrieval accuracy of VPRS model is satisfying (overall accuracy: 87.32% and KHAT: 0.84) and better than original data. Moreover, data dimension has been decreased dramatically (from 12 to 3) and key attributes found by the model may be useful for specific retrieval in wetland inventories.     

Recovery of forest structure and spectral properties after selective logging in lowland Bolivia.

Effective monitoring of selective logging from remotely sensed data requires an understanding of the spatial and temporal thresholds that constrain the utility of those data, as well as the structural and ecological characteristics of forest disturbances that are responsible for those constraints. Here we assess those thresholds and characteristics within the context of selective logging in the Bolivian Amazon. Our study combined field measurements of the spatial and temporal dynamics of felling gaps and skid trails ranging from <1 to 19 months following reduced-impact logging in a forest in lowland Bolivia with remote-sensing measurements from simultaneous monthly ASTER satellite overpasses. A probabilistic spectral mixture model (AutoMCU) was used to derive per-pixel fractional cover estimates of photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV), and soil. Results were compared with the normalized difference in vegetation index (NDVI). The forest studied had considerably lower basal area and harvest volumes than logged sites in the Brazilian Amazon where similar remote-sensing analyses have been performed. Nonetheless, individual felling-gap area was positively correlated with canopy openness, percentage liana coverage, rates of vegetation regrowth, and height of remnant NPV. Both liana growth and NPV occurred primarily in the crown zone of the felling gap, whereas exposed soil was limited to the trunk zone of the gap. In felling gaps >400 m2, NDVI, and the PV and NPV fractions, were distinguishable from unlogged forest values for up to six months after logging; felling gaps <400 m2 were distinguishable for up to three months after harvest, but we were entirely unable to distinguish skid trails from our analysis of the spectral data.     

Temporal variability of forest fires in eastern Amazonia

Widespread occurrence of fires in Amazonian forests is known to be associated with extreme droughts, but historical data on the location and extent of forest fires are fundamental to determining the degree to which climate conditions and droughts have affected fire occurrence in the region. We used remote sensing to derive a 23-year time series of annual landscape-level burn scars in a fragmented forest of the eastern Amazon. Our burn scar data set is based on a new routine developed for the Carnegie Landsat Analysis System (CLAS), called CLAS-BURN, to calculate a physically based burn scar index (BSI) with an overall accuracy of 93% (Kappa coefficient 0.84). This index uses sub-pixel cover fractions of photosynthetic vegetation, non-photosynthetic vegetation, and shade/burn scar spectral end members. From 23 consecutive Landsat images processed with the CLAS-BURN algorithm, we quantified fire frequencies, the variation in fire return intervals, and rates of conversion of burned forest to other land uses in a 32 400 km2 area. From 1983 to 2007, 15% of the forest burned; 38% of these burned forests were subsequently deforested, representing 19% of the area cleared during the period of observation. While 72% of the fire-affected forest burned only once during the 23-year study period, 20% burned twice, 6% burned three times, and 2% burned four or more times, with the maximum of seven times. These frequencies suggest that the current fire return interval is 5-11 times more frequent than the estimated natural fire regime. Our results also quantify the substantial influence of climate and extreme droughts caused by a strong El Ni?o Southern Oscillation (ENSO) on the extent and likelihood of returning forest fires mainly in fragmented landscapes. These results are an important indication of the role of future warmer climate and deforestation in enhancing emissions from more frequently burned forests in the Amazon.     

Impacts of residential development on vegetation cover for a remote coastal barrier in the Outer Banks of North Carolina, USA

Coastal barrier environments are heavily influenced by human activities yet there are few examples of landscape ecological work investigating human dimensions of settlement disturbance patterns and processes. We investigated the impacts of residential development on vegetation cover for a remote roadless coastal barrier in Carova, North Carolina that is subject to policies from the federal to local levels and addressed three research questions: How has the region’s the policy history influenced patterns of residential parcel development? What are the spatial and temporal patterns of parcel development? How has development impacted patterns of barrier vegetation cover? We traced the influences of the federal 1982 Coastal Barrier Resources Act (CBRA) designed to discourage development in risky coastal areas as well as state/local coastal policies and employed remote sensing change detection, NDVI analysis and spatial analysis and regression techniques. Results showed an acceleration of new housing structures since 1990, contrary to the intended effects of CBRA. An estimated vegetation cover loss of 437 m² was associated with each newly developed parcel. NDVI varied along spatial and temporal gradients with more recent development having lower NDVI than older development. Recently developed parcels were larger in area, closer to the beach, and contained houses with larger footprints compared to older developed parcels. Our approach represents a place-based analytical framework for coastal barrier landscapes. Beyond the Carova case study, adopting such an approach coupling natural and human systems for the entire eastern US barrier system requires defining a comprehensive set of coastal barrier spatial units to enable typological classification and subsequent systematic investigation to inform debates regarding coastal ecosystem services and sustainability.     

Identification of hot spots and well managed areas of Pichavaram mangrove using Landsat TM and Resourcesat—1 LISS IV: an example of coastal resource conservation along Tamil Nadu Coast, India

The present work is a multi-temporal satellite based study on the spatial dynamic of an important coastal habitat, the Pichavaram mangrove ecosystem, over a period of 15 years. The Pichavaram mangrove forest near Chidambaram, South India is the second largest mangrove forest in the world. Unsupervised classification, the Iterative Self Organising Data Analysis Technique (ISODATA), has been used to classify the mangrove cover into the open and dense classes. The status of the classes has been monitored using Landsat TM of 1991, 2001, and Resourcesat–1 LISS IV of 2006 satellite data. The study demonstrated that by classifying mangrove ecosystem into just the 3 classes using remote sensing data and by studying their temporal variations, it is possible to get a reasonably accurate picture of the extent and condition of the mangrove ecosystem. The total area of the Pichavaram mangrove showed a net increase of 2.51 km² within a span of 15 years (1991 to 2006). The hot spots that are at a risk of being degraded, and on the other hand, the mangrove areas that are well managed are identified using Geographical Information System (GIS) tools for the restoration and conservation measures.     

Survey methods for assessing land cover map accuracy

The increasing availability of digital photographic materials has fueled efforts by agencies and organizations to generate land cover maps for states, regions, and the United States as a whole. Regardless of the information sources and classification methods used, land cover maps are subject to numerous sources of error. In order to understand the quality of the information contained in these maps, it is desirable to generate statistically valid estimates of accuracy rates describing misclassification errors. We explored a full sample survey framework for creating accuracy assessment study designs that balance statistical and operational considerations in relation to study objectives for a regional assessment of GAP land cover maps. We focused not only on appropriate sample designs and estimation approaches, but on aspects of the data collection process, such as gaining cooperation of land owners and using pixel clusters as an observation unit. The approach was tested in a pilot study to assess the accuracy of Iowa GAP land cover maps. A stratified two-stage cluster sampling design addressed sample size requirements for land covers and the need for geographic spread while minimizing operational effort. Recruitment methods used for private land owners yielded high response rates, minimizing a source of nonresponse error. Collecting data for a 9-pixel cluster centered on the sampled pixel was simple to implement, and provided better information on rarer vegetation classes as well as substantial gains in precision relative to observing data at a single-pixel.     

植被覆盖度与沙尘暴形成条件分析

以归一化差值植被指数（Normalized Difference Vegetation Index,NDVI）为指标分析了我国1981年到1998年期间植被覆盖变化情况,给出了植被急剧减少产生新沙源的实例。通过对1998年4月强沙尘暴的分析,指出影响沙尘暴产生和运移的因素除了地形、风速、风向、大气稳定度外,当地植被覆盖和土壤因素也是关键因素之一。并通过对植被覆盖度（NDVI）与浮尘指数（Aerosol Index）进行定量分析,发现二者之间存在明显的负相关关系,表明植被的存在可以从一定程度上抑制浮尘现象的产生。因此,在沙尘运移路径上,采取封山封荒,植树种草,增加植被覆盖度以固结当地土壤和泥沙的措施,可以在一定程度上抑制沙尘暴灾害的发生。     

气候因子与植被的时滞相关分析——以广西为例

广西地区地下河发育,地表水缺乏,生态系统脆弱、抗干扰能力低,灾害频繁,森林生态系统严重退化,石漠化程度加剧,属于生态脆弱区域,而植被变化是生态系统对气候变化响应的指示器,其变化研究对该地区生态环境建设具有重要意义。文章基于1999--2010年气温与降水数据和GIMMS．NDVI（GlaobalInventoryModellingandMappingMtudies-NormalDifferenceVegetionIndex）数据集,研究近12年来广西地区植被覆盖时空特征。通过时滞相关分析法,分析研究区不同植被类型受气温和降水的影响,即植被NDVI对气温和降水的响应程度。结果表明,（1）植被NDVI与温度的时滞相关程度强于降水,而响应时间刚好相反,植被NDVI对降水的响应比对温度的响应程度要快。（2）植被NDvI与降水的时滞相关规律呈桂南较弱—桂中较强—桂北较弱,但响应时间刚好相反,即桂南较快一桂中较慢—桂北较快。（3）植被NDvI与温度的时滞相关性变化规律呈由南向北递减,响应时间由南向北变快。（4）植被NDVI与气候因子时滞相关越强,响应的时间就越迟缓,反之越快。（5）不同的植被类型对水热条件响应程度不同,与水热条件时滞相关越强的植被则滞后时间相对越久。     

南疆西部不同植被类型NDVI对风沙灾害的响应

不同植被类型对风沙灾害的响应研究有助于风沙灾害的遥感监测评估和防灾减灾工作。采用传统统计学和空间自相关方法,利用MODIS的每日反射率和土地类型产品,分析了风沙灾害后南疆西部不同植被类型NDVI的变化规律及其空间分布特征。结果表明：风沙灾害后,研究区不同植被类型NDVI的差异在增大,NDVI变化值在一0．3865~0．4148之间,NDVI减小的面积占整个研究区面积的54．98％,变异系数增大值在2．44％～36．75％之间;受到风沙灾害的影响,研究区植被NDVI全局Moran’SI系数从0．7982减小到0．6786,但在空间上仍存在显著的正相关性,具有显著的空间集聚特征;由NDVI差值的局部空间自相关指标集聚图以及不同植被类型NDVI差值的空间关联区域面积百分比,可以发现落叶针叶林和裸地或低植被覆盖地受风沙灾害的影响较小,郁闭灌丛和作物受风沙灾害的影响较大。上述关于风沙灾害后南疆西部不同植被类型的变化及其空间格局的研究结果,不仅证明了防护林的风沙阻挡作用,也可以为风沙灾害的防治工作提供参考依据。     

Model estimation of land-use effects on water levels of northern prairie wetlands.

Wetlands of the Prairie Pothole Region exist in a matrix of grassland dominated by intensive pastoral and cultivation agriculture. Recent conservation management has emphasized the conversion of cultivated farmland and degraded pastures to intact grassland to improve upland nesting habitat. The consequences of changes in land-use cover that alter watershed processes have not been evaluated relative to their effect on the water budgets and vegetation dynamics of associated wetlands. We simulated the effect of upland agricultural practices on the water budget and vegetation of a semipermanent prairie wetland by modifying a previously published mathematical model (WETSIM). Watershed cover/land-use practices were categorized as unmanaged grassland (native grass, smooth brome), managed grassland (moderately heavily grazed, prescribed burned), cultivated crops (row crop, small grain), and alfalfa hayland. Model simulations showed that differing rates of evapotranspiration and runoff associated with different upland plant-cover categories in the surrounding catchment produced differences in wetland water budgets and linked ecological dynamics. Wetland water levels were highest and vegetation the most dynamic under the managed-grassland simulations, while water levels were the lowest and vegetation the least dynamic under the unmanaged-grassland simulations. The modeling results suggest that unmanaged grassland, often planted for waterfowl nesting, may produce the least favorable wetland conditions for birds, especially in drier regions of the Prairie Pothole Region. These results stand as hypotheses that urgently need to be verified with empirical data.     

榆林地区植被时空分异特征及其影响因素研究

植被作为反映陆地生态系统和气候的重要指标,对研究全球或区域生态环境变化具有重要作用.以地处黄土高原生态脆弱区的榆林市为研究区,基于地理探测器模型,选取坡向、坡度、气温、降水和土壤类型5类自然因子,土地利用类型、人口密度和GDP 3类人文因子,分析榆林地区植被空间分异特征及其驱动力,并揭示了促进植被生长影响因子的最适宜特...     

Post-fire vegetation regrowth detection in the Deiva Marina region (Liguria-Italy) using Landsat TM and ETM+ data

Obtaining quantitative information about the recovery of fire-affected ecosystems is of utmost importance from the management and decision-making point of view. Nowadays the concern about natural environment protection and recovery is much greater than in the past. However, the resources and tools available for its management are still not sufficient. Thus, attention and precision is needed when decisions must be taken. Quantitative estimates on how the vegetation is recovering after a fire can be of help for evaluating the necessity of human intervention on the fire-affected ecosystem, and their importance will grow as the problem of forest fires, climate change and desertification increases.This article performs a comparison of methods to extract quantitative estimates of vegetation cover regrowth with Landsat TM and ETM+ data in an area that burned during the summer of 1998 in the Liguria region (Italy). In order to eliminate possible sources of error, a thorough pre-processing was carried out, including a careful geometric correction (reaching RMSE lower than 0.3 pixels), a topographic correction by means of a constrained Minnaert model and a combination of absolute and relative atmospheric correction methods. Pseudo Invariant Features (PIF) were identified by implementing an automated selection method based in temporal Principal Component Analysis (PCA), which has been called multi-Temporal n-Dimensional Principal Component Analysis (mT-nD-PCA).Spectral Mixture Analysis (SMA) was compared against quantitative vegetation indices which are based on well known traditional vegetation indices like Normalized Difference Vegetation Index (NDVI) and Modified Soil Adjusted Vegetation Index (MSAVI). Accuracy assessment was performed by regressing vegetation cover results obtained with each method against field data gathered during the fieldwork campaign carried out in the study area. Results obtained showed how vegetation cover fractions extracted from the NDVI based quantitative index were the most accurate, being superior to the rest of the techniques applied, including SMA.     

Study on the utility of IRS 1D LISS-III data and the classification techniques for mapping of Sunderban mangroves

Mangrove conservation and management is a stupendous task chiefly due to the inaccessibility and the hostile substrate conditions. Remote sensing technology serves as an important tool in providing fast, accurate and up-to-date baseline information on the status of mangroves. It is almost impossible to carry out conventional field surveys in these swampy areas. The present study aims at the classification and mapping of the mangroves in Sunderban Biosphere Reserve (SBR) in the West Bengal province of India using IRS 1D LISS-III satellite data. Different classification approaches, viz., on-screen visual interpretation, supervised and unsupervised classifications were tried. The study showed that four mangroves classes, viz., Avicennia, Phoenix, mixed mangroves, and mangrove scrub and eight non-mangrove classes could be delineated using all the three approaches. All the mangrove and non-mangrove classes were field verified and the overall accuracy as well as user’s and producer’s accuracies for each category were determined. It was observed that among the three approaches, on-screen visual interpretation yielded higher classification accuracy (91.67%) compared to supervised (79.90%) and unsupervised classifications (71.08%). The results obtained through on-screen visual interpretation showed that all mangrove categories together cover 23.21% of the total geographical area of SBR, of which the mixed mangrove category covers maximum area (18.31%). Among the non-mangrove classes, the waterbody occupies largest area (35.36%) followed by agriculture (34.51%).     

基于GIS的祁连山植被NDVI对气温降水的旬响应分析

利用祁连山地区SPOTVGT-NDVI数据和气象站点旬平均气温、降水资料,运用最大化合成、趋势线分析和相关分析方法分析了祁连山植被的时空变化趋势,并从旬时间尺度上分析了祁连山植被对气温和降水的响应特征。结果表明:近10年来,祁连山年最大化植被NDVI增加了3.1%,植被改善、无变化和退化的面积分别占总面积的70.21%、21.44%和8.35%。祁连山植被NDVI对气温变化的响应大于降水,对气温和降水变化的最大响应滞后都为2旬左右。秋季植被NDVI对气温和降水变化响应最大,夏季植被NDVI对气温和降水响应的滞后期长于春季和秋季。祁连山植被NDVI对气温和降水变化的旬最大响应表现为中段大于东段和西段,NDVI对气温和降水变化的最大响应滞后期呈现出西段中段东段的空间分布特征。     

A pilot study on the effect of Cu,Zn, and Cd on the spectral curves and chlorophyll of wheat canopy at tiller stage

The aim of this study was to investigate canopy spectral reflectance responses to different levels of heavy metals copper (Cu), zinc (Zn), and cadmium (Cd) induced stress. Random blocks design experiment was conducted to simulate Cu, Zn, and Cd at five concentration levels. Chlorophyll and visible and near infrared canopy reflectance were measured for each treatment 56 days after seeding using chlorophyll meter SPAD-502 and spectroradiometer, respectively. New vegetative indices termed ratio of inclination angles (RIA), MERIS (the medium resolution imaging spectrometer) terrestrial chlorophyll index (MTCI), and normalized difference vegetation index (NDVI) were used to assess the response of canopy spectral reflectance to different heavy metal levels. Significant spectral variability especially in blue, red, and near infrared reflectance was observed for different heavy metal treatments. One-sample test showed significant difference for NDVI, MTCI, and RIA among five-level treatments at the 0.01 level. Correlation analysis demonstrated that the two vegetative indices (RIA and MTCI) were significantly correlated with chlorophyll meter values for Cd, Cu, and Zn treatments. However, NDVI was only significantly correlated with chlorophyll meter values for Cu and Zn treatments. Linear regression analysis also illustrated that RIA and MTCI were potential indices for predicting chlorophyll concentration with significant F test under Cu-, Zn-, and Cd-mediated stress.     

Measuring Protected‐Area Isolation and Correlations of Isolation with Land‐Use Intensity and Protection Status

Abstract: Protected areas cover over 12% of the terrestrial surface of Earth, and yet many fail to protect species and ecological processes as originally envisioned. Results of recent studies suggest that a critical reason for this failure is an increasing contrast between the protected lands and the surrounding matrix of often highly altered land cover. We measured the isolation of 114 protected areas distributed worldwide by comparing vegetation‐cover heterogeneity inside protected areas with heterogeneity outside the protected areas. We quantified heterogeneity as the contagion of greenness on the basis of NDVI (normalized difference vegetation index) values, for which a higher value of contagion indicates less heterogeneous land cover. We then measured isolation as the difference between mean contagion inside the protected area and mean contagion in 3 buffer areas of increasing distance from the protected‐area border. The isolation of protected areas was significantly positive in 110 of the 114 areas, indicating that vegetation cover was consistently more heterogeneous 10–20 km outside protected areas than inside their borders. Unlike previous researchers, we found that protected areas in which low levels of human activity are allowed were more isolated than areas in which high levels are allowed. Our method is a novel way to assess the isolation of protected areas in different environmental contexts and regions.     

Territory choice during the breeding tenure of male sedge warblers

A territorial male can shift the location of its territory from year to year in order to increase its quality. The male can base its decision on environmental cues or else on its breeding experiences (when territory shift is caused by breeding failure in previous seasons). We tested these possible mechanisms of territory choice in the sedge warbler (Acrocephalus schoenobaenus), a territorial migrating passerine that occupies wetlands. This species bases its territory choices on an environmental cue: tall wetland vegetation cover. We found that the magnitude of territory quality improvement between seasons (measured as the area of tall wetland vegetation) increased throughout the early stages of a male's breeding career as a result of territory shifts dependent on the earliness of arrival. The distance the territory was shifted between seasons depended negatively on the previous year's territory quality and, less clearly, on the previous year's mating success. On the other hand, previous mating or nesting success had no influence on territory quality improvement between seasons as measured in terms of vegetation. The results imply that tall wetland vegetation is a long-term, effective environmental cue and that a preference for territories in which this type of landcover prevails has evolved into a rigid behavioral mechanism, supplemented by short-term individual experiences of breeding failure.     

Effects of human activities (raking,scraping, off-road vehicles) and natural resource protections on the spatial distribution of beach vegetation and related shoreline features in New Jersey

This study examined the relative impacts of different human activities and natural resource protections on the spatial distribution of beach vegetation and related habitat features (wrack, dune succession) in New Jersey (USA). Field surveys of the 209-km shoreline categorized beach segments according to vegetation cover classes, human activities, protection measures (exclosures, beach management plans, access restrictions) and ownership status (federal, state, etc.). A partition model (classification tree) was used to confirm the relative dominance hierarchy of human actions on the distribution of beach vegetation observed, and quantitative comparisons of dominant activities were conducted using vegetation data collected on 218 transects. The spatial extent of beach vegetation was found to be severely restricted by human activities when unconstrained by resource protections. The greatest reductions were found to result from mechanical raking (?99 %), scraping (?91 %) and all-year recreational ORV use (?86 %), which were dominant on nearly 70 % of the state shoreline. Beaches containing larger areas of vegetation (>5 m) were concentrated in areas with resource protections of various kinds (99 %), and on federal or other public parklands (68 %). Exclosures resulted in the greatest coverage of vegetation (48 % of beach surface) compared to public access restricted areas (41 %), beach management plans (31 %), government-only ORV use (31 %), and off-season recreational ORVs (15 %). Greater protection and recovery of beach vegetation and habitat is needed for species conservation and erosion protection in New Jersey and other coastal environments where these activities occur.     

1987-2008年南四湖湿地植被碳储量时空变化特征

利用研究区1987、1997和2008年的LandsatTM／ETM＋卫星遥感数据,结合实地采样调查和收集的资料,计算3个时期山东省南四湖湿地各植被类型的面积和碳储量;同时依照不同覆被类型（植被类型）的平均碳储量将各覆被类型的斑块划分为不同储碳等级,绘制3个时期的南四湖湿地植被碳格局图,分析植被碳储量的时空变化特征、变化产生的原因和变化对湿地的潜在影响。研究结果表明：研究前期（1987年）植被储碳格局存在着明显的植被碳储量以湖南北中心线向两岸增大的带状特征,研究中后期（1997-2008年）带状储碳格局不断破碎化并最终消失。整个研究期高储碳等级的斑块面积不断减小,低储碳等级的斑块面积增加。湿地植被碳储量呈现前期基本稳定后期显著减少的特征,1987、1997和2008年湿地植被碳储量均值分别为1．07、1．08和O．64TgC,1987-1997年湿地植被碳储量平均年增加0．001TgC,变化幅度不大;1997-2008年年减少0．04TgC,下降较明显。其中自然植被碳储量在整个研究期内持续减少,人工植被碳储量呈现先增加后减少的波动变化特征,分析认为这一变化特征产生的主要原因是南四湖地区多因子驱动的土地覆被变化活动。通过区域湿地植被的碳平衡动态分析认为,植被碳储量（碳库）的减少可能会导致整个湿地碳储量的入不敷出,使整个湿地碳汇能力下降甚至可能变为碳源。