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Question: How are plant communities of the Flooding Pampa grasslands spatially distributed? How do canopy dynamics of the different communities vary among seasons and years? Location: Buenos Aires province, Argentina. Methods: We characterized the distribution of communities through a supervised classification based on four Landsat 5TM images. We sampled species composition of 200 sites, with 130 of them corresponding to natural communities. Of the sampling areas 60% were used to classify, and the remaining areas to assess classification accuracy. We characterized the seasonal and interannual variability of canopy dynamics using NDVI (Normalized Difference Vegetation Index) data provided by MODIS /Terra images. Results: Overall accuracy of the classification was satisfactory. The resulting maps showed a landscape formed by a matrix of extended lowlands with small patches of mesophytic and humid mesophytic meadows. The October scene (near the peak of productivity) was particularly important in discriminating among communities. The seasonal pattern of NDVI differed among communities and years. Mesophytic meadows had the highest NDVI mean and the lowest interannual coefficient of variation, halophytic steppes had the lowest mean, and vegetated ponds were the most variable. Conclusions: These grasslands have a fine‐grained heterogeneity at the landscape scale. Each plant community has distinct seasonal and interannual canopy dynamics. These two features of grassland structure and functioning represent key information for rangeland management that may be obtained through a combination of minor field sampling and remote sensing.  相似文献   

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Aims Fujian Province has been one of the most severe soil erosion regions since Ming and Qing Dynasty in China. Recently, several ecological restoration projects have been implemented and they have significantly changed vegetation cover in this region. Methods We analyzed the four-decade vegetation cover change in Fujian Province using seven time-series data of Landsat Multispectral Scanner (MSS), Thematic Mapper (TM), and Operational Land Imager (OLI) between 1975 and 2014. We further explored the possible drivers on vegetation cover change by incorporating statistical data of plantation, cropland and urbanized area. Important findings Vegetation coverage in Fujian Province has increased from 69.0% to 77.8% between 1975 and 2014. However, a slight decrease was observed between 1995 and 2005. Spatially, forest was the primary vegetation type in the northwest, where croplands and human settlements were scattered along rivers or oceans. Shrubs and bare lands were also scattered across the northwest. In southwest, the areas of bare land, shrub land and cropland decreased, while areas of forest and human settlements expanded. The vegetation coverage and urbanized area increased at the cost of cropland and bare land.  相似文献   

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Question: Coastal dune systems are characterized by a natural mosaic that promotes species diversity. This heterogeneity often represents a severe problem for traditional mapping or ground survey techniques. The work presented here proposes to apply a very detailed CORINE land cover map as baseline information for plant community sampling and analysis in a coastal dune landscape. Location: Molise coast, Central Italy. Method: We analysed through an error matrix the coherence between land cover classes and vegetation types identified through a field survey. The CORINE land cover map (scale 1: 5000) of the Molise coast was used with the CORINE legend expanded to a fourth level of detail for natural and semi‐natural areas. Vegetation data were collected following a random stratified sampling design using the CORINE land cover classes as strata. An error matrix was used to compare, on a category‐by‐category basis, the relationship between vegetation types (obtained by cluster analyses of sampling plots) and land cover classes of the same area. Results: The coincidence between both classification approaches is quite good. Only one land cover class shows a very weak agreement with its corresponding vegetation type; this result was interpreted as being related to human disturbance. Conclusions: Since it is based on a standard land cover classification, the proposal has a potential for application to most European coastal systems. This method could represent a first step in the environmental planning of coastal systems.  相似文献   

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The impact of Land use/land cover (LULC) change was assessed through monitoring the distribution of ecological indicators and tracking the aeolian deposits, which provides valuable information on desertification and climate change in Tunisian arid regions. This study was conducted in Oum Zessar area, in southeastern Tunisia. Both visual interpretation and automated classification approach were developed to extract sand features using Landsat images for 2000, 2008 and 2014. The automated classification includes a decision tree classifier (DT) and an unsupervised classification applied to the principal components extracted from Knepper ratios composite. The validation of the classification methods showed that the DT had an overall accuracy over 84%. The results of the change detection have shown an increase in the classes of Agriculture behind tabia by 10.68%, the rangelands and croplands by 24.37% and the mountain rangelands by 14.93%, and a decrease in the classes of Agriculture behind jessour by 33.65%, sand encroachments by 12.93% and halophyte rangelands by 3.4%, respectively. These resulting maps seem to be the suitable decision-support tools for management of land use in arid regions of Tunisia, in particular, for land degradation assessment and water and soil conservation.  相似文献   

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Understanding temporal and spatial dimensions of land cover dynamics is a critical factor to link ecosystem transformation to land and environmental management. The trajectory of land cover change is not a simple difference between two conditions, but a continuous process. Therefore, there is a need to integrate multiple time periods to identify slow and rapid transformations over time. We mapped land cover composition and configuration changes using time series of Landsat TM/ETM+ images (1985–2011) in Southern Chile to understand the transformation process of a temperate rainforest relict and biodiversity hotspot. Our analysis builds on 28 Landsat scenes from 1985 to 2011 that have been classified using a random forests approach. Base on the high temporal data set we quantify land cover change and fragmentation indices to fully understand landscape transformation in this area. Our results show a high deforestation process for old growth forest strongest at the beginning of the study period (1985–1986–1998–1999) followed by a progressive slowdown until 2011. Within different study periods deforestation rates were much larger than the average rate over the complete study period (0.65%), with the highest annual deforestation rate of 1.2% in 1998–1999. The deforestation resulted in a low connectivity between native forest patches. Old-growth forest was less fragmented, but was concentrated mainly in two large regions (the Andes and Coastal mountain range) with almost no connection in between. Secondary forest located in more intensively used areas was highly fragmented. Exotic forest plantation areas, one of the most important economic activities in the area, increased sevenfold (from 12,836 to 103,540 ha), especially during the first periods at the expense of shrubland, secondary forest, grassland/arable land and old grown forest. Our analysis underlines the importance of expanding temporal resolution in land cover/use change studies to guide sustainable ecosystem management strategies as increase landscape connectivity and integrate landscape planning to economic activities. The study is highlighting the key role of remote sensing in the sustainable management of human influenced ecosystems.  相似文献   

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Abstract. We present a remote sensing based vegetation mapping technique well suited to a heterogeneous, semi‐arid environment. 10 structural vegetation classes were identified and described on the ground. Using Landsat‐TM from two different seasons and a combination of three conventional classification techniques (including a multi‐temporal classification) we were unsuccessful in delineating all of the desired vegetation classes. We then employed a simple tex‐tural classification index, known as the Moving Standard Deviation Index (MSDI), that has been used to map degradation status. MSDI measures spatial variations in the landscape and is calculated by passing a 3 × 3 standard deviation filter across the Landsat‐TM red band. High MSDI values are associated with degraded or disturbed rangelands whilst low MSDI values are associated with undisturbed rangeland. A combination of two conventional multi‐spectral techniques and MSDI were used to produce a final vegetation classification at an accuracy of 84 %. MSDI successfully discriminated between two contrasting vegetation types of identical spectral properties and significantly strengthened the accuracy of the classification. We recommend the use of a tex‐tural index such as MSDI to supplement conventional vegetation classification techniques in heterogeneous, semi‐arid or arid environments.  相似文献   

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The urbanization of watersheds is a highly dynamic global phenomenon that must be monitored. With consequences for the environment, the population, and the economy, accurate products at adequate spatial and temporal resolutions are required and demanded by the science community and stakeholders alike. To address these needs, a new Impervious Surface Area (ISA) product was created for a Portuguese Watershed (Mondego river) from Landsat data (a combination of leaf-on multispectral bands, derived products, and NDVI time series), using Regression Tree Models (RTM). The product provides 30-m spatial resolution ISA estimates (0–100%) with a Mean Average Error (MAE) of 1.6% and Root Mean Square Error (RMSE) of 5.5%.A strategy to update the baseline product was tested in earlier imagery (2001 and 2007) for a subset of the watershed. Instead of updating the baseline product, the strategy seeks to identify stable training samples and remove those where change was detected in a time series of Change Vector Analysis (CVA). The stable samples were then used to create new ISA models using RTM. The updated maps were similar to the original product in terms of accuracy metrics (MAE: 2001: 2.6%; 2007:3.6%).The products and methodology offer a new perspective on the urban development of the watershed, at a scale previously unavailable. It can also be replicated elsewhere at a low cost, leveraging the growing Landsat data archive, and provide timely information on relevant land cover metrics to the scientific community and stakeholders.  相似文献   

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  总被引:6,自引:0,他引:6  
Abstract. Vegetation phenological phenomena are closely related to seasonal dynamics of the lower atmosphere and are therefore important elements in global models and vegetation monitoring. Normalized difference vegetation index (NDVI) data derived from the National Oceanic and Atmospheric Administration's Advanced Very High Resolution Radiometer (AVHRR) satellite sensor offer a means of efficiently and objectively evaluating phenological characteristics over large areas. Twelve metrics linked to key phenological events were computed based on time-series NDVI data collected from 1989 to 1992 over the conterminous United States. These measures include the onset of greenness, time of peak NDVI, maximum NDVI, rate of greenup, rate of senescence, and integrated NDVI. Measures of central tendency and variability of the measures were computed and analyzed for various land cover types. Results from the analysis showed strong coincidence between the satellite-derived metrics and predicted phenological characteristics. In particular, the metrics identified interannual variability of spring wheat in North Dakota, characterized the phenology of four types of grasslands, and established the phenological consistency of deciduous and coniferous forests. These results have implications for large-area land cover mapping and monitoring. The utility of remotely sensed data as input to vegetation mapping is demonstrated by showing the distinct phenology of several land cover types. More stable information contained in ancillary data should be incorporated into the mapping process, particularly in areas with high phenological variability. In a regional or global monitoring system, an increase in variability in a region may serve as a signal to perform more detailed land cover analysis with higher resolution imagery.  相似文献   

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In regions with thousands of lakes, large scale regional macrophyte surveys are rarely done due to logistical difficulties and high costs. We examined whether remote sensing can be used for regional monitoring of macrophytes in inland lakes using a field study of 13 lakes in Michigan, USA (nine model development lakes and four model testing lakes). Our objectives were: (1) to determine if different levels of macrophyte cover, different growth forms or specific species could be detected using the Landsat-5 TM sensor, and (2) to determine if we could improve predictions of macrophyte abundance and distribution in lakes by including sediment type or measures of water clarity (Secchi disk transparency, chlorophyll a, phytoplankton biovolume, or water color) in our models. Using binomial and multinomial logistic regression models, we found statistically significant relationships between most macrophyte measures and Landsat-5 TM values in the nine model development lakes (percent concordant values: 58–97%). Additionally, we found significant correlations between three lake characteristics and the TM values within lake pelagic zones, despite the inability of these variables to improve model predictions. However, model validation using four lakes was generally low, suggesting caution in applying these models to other lakes. Although the initial model development results suggest that remote sensing is a potentially promising tool for regionally assessing macrophytes, more research is necessary to refine the models in order for them to be applied to unsampled lakes.  相似文献   

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2009年3~7月,在西藏自治区墨竹工卡县日多乡念村(29o46′N,92o19′~92o20′E,海拔4 423~5 015 m),采用目标动物抽样法对高原鼠兔(Ochotona curzoniae)的警戒行为和取食行为进行了观察,利用广义线性模型(GLM)的泊松回归模型分析了不同土地利用、植被覆盖度、植物类别和植被优势种对高原鼠兔行为影响的差异。研究结果表明,雌性高原鼠兔取食行为频率高于雄性(β=﹣0.203,SE=0.096,P0.05),警戒行为频率低于雄性(β=0.199,SE=0.088,P0.05)。高原鼠兔的取食行为(β=﹣0.009,SE=0.004,P0.05)随着栖息地内双子叶植物覆盖比例增高而呈递减趋势,相应地,随着栖息地内单子叶植物覆盖比例的增高呈现递增趋势(β=0.009,SE=0.004,P0.05)。高原鼠兔的警戒行为在放牧地(β=0.273,SE=0.131,P0.05)以及植被覆盖度高的栖息地内(β=0.007,SE=0.003,P0.05)均呈递增趋势。随着薹草属植物覆盖度比例增高,高原鼠兔的取食行为(β=0.023,SE=0.006,P0.001)呈现递增趋势,而警戒行为呈递减趋势(β=﹣0.018,SE=0.007,P0.05)。  相似文献   

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Aim To develop a new method for bioclimate mapping where the vegetation layer is the main source of climate information. Location The study area includes four subareas, all situated on the Varangerhalvøya peninsula in Finnmark, north‐easternmost Norway (70–71° N). The four subareas were chosen to represent most of the climatic, topographic, geomorphologic and botanic diversity along the arctic–boreal gradient in the area. The four meteorological stations in the area show a climatic gradient with mean July temperature ranging from 10.1 to 12.3 °C. Methods The new vegetation‐based method is based on the fact that most plant species and plant communities both in the Arctic and adjacent areas have a distribution pattern limited by temperature to some extent. The vegetation is mapped using Landsat TM data and a contextual correction process in a geographic information system. The mapped vegetation units are defined as temperature indicators based on their total distribution patterns and the temperature indicator value of their high frequency and dominant species. The indicator value and degree of cover of all thermophilous vegetation units, within each 500 × 500 m study unit, are combined in a Vegetation‐based Index of Thermophily, VItm. This new vegetation‐based method is based on the same basic idea as a recently published floristic‐based method for calculating a Floristic‐based Index of Thermophily, FItm. The VItm values are tested by comparison with the FItm values, and temperature data collected in the field during two growing seasons, and the differences are interpreted ecologically. Results Twenty‐one of the mapped vegetation units were defined as thermophilous and categorized in five groups of temperature indicators. The VItm values showed a strong positive linear relationship with the temperatures measured during the years 2001 and 2002, with r2 values of 0.79 and 0.85, respectively. The VItm values show a high linear relationship (r2 = 0.76) with the 71 study units where the FItm values were calculated. As interpreted from the relationship with temperature measurements and FItm values, the vegetation‐based method seems to work at a broad range of ecological conditions, with very dry, acidic sites being the most important exception. The VItm values are related to growing degree‐days of a normal year, and the four subareas are mapped, showing a diversity of 13 bioclimatic classes. The birch forest line is estimated to occur at about 980 °C‐days. The results show climatic gradients with temperatures increasing from the cold coast towards the interior, from wind‐exposed convex hills towards wind‐protected valleys, and from mountain plateaux towards south‐facing lowlands. The north‐easternmost study site at the coast is positioned within the arctic shrub tundra zone. Main conclusions The vegetation‐based method shows a strong positive correlation both with measured temperatures and the floristic‐based method within a broad range of different ecological conditions. The vegetation‐based method has the potential for bioclimatic mapping of large areas in a cost‐effective way. The floristic‐based method has higher accuracy and is more flexible than the vegetation‐based method, and the two methods seem to complement each other.  相似文献   

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以科尔沁沙地沙丘—草甸过渡带区域主要土地覆被类型为研究对象,以1987—2017年多时相Landsat TM/OLI遥感影像解译分类为基础,参考生态学植被演替研究方法,系统分析研究区30年来的土地利用/覆被动态演变规律,研究结果表明:(1)决策树法在复杂下垫面不同覆被类型的同步识别效果较好,所有影像分类精度均达到88%以上,分类效果较好,其中2017年分类精度最高为95.24%,达到了分类研究的要求;(2)研究区存在着\"半灌丛-草甸地-灌丛\"的植被结构特征,且整体表现为\"南进北退\"的变化趋势。结合土地利用动态度分析结果表明人类活动干涉下,研究区整体上遵循了半干旱区植被条件改善的一般规律,侧面反映该研究区域生态环境的持续不稳定性和脆弱性;(3)研究区覆被类型发生变化的总面积达到2623.59 hm2,总变化强度为63.76%。其中正向演替的比例为52.61%,以半灌丛面积的持续减小与沙地草甸面积的持续扩张为主要变化特征。但同时,半灌丛转为沙地的面积为184.95 hm2,表明以放牧为主的研究区同时发生着局部的逆行演变;(4)质心迁移结果反映了1987—2017年间,除人为影响较大的林地、草地以及耕地向北迁移外,其他植被类型的质心都有很明显的南迁,主要植被类型重心迁移距离依次由大到小为耕地>半灌丛>灌丛>沙地草甸>湿地草甸>林地。研究通过记录科尔沁沙地连续扩展的时空模式,展示了遥感—生态和时间序列影像在30 m分辨率下跟踪土地利用/覆被变化的潜力,为提高干旱半干旱区土地利用情况的动态监测效率,开展土地利用/覆被动态演变研究提供参考。  相似文献   

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  总被引:10,自引:0,他引:10  
The biomass and biomass dynamics of forests are major uncertainties in our understanding of tropical environments. Remote sensing is often the only practical means of acquiring information on forest biomass but has not always been used successfully. Here the conventional approaches to the estimation of forest biomass from remotely sensed data were evaluated relative to techniques based on the application of artificial neural networks. Together these approaches were used to estimate and map the biomass of tropical forests in north‐eastern Borneo from Landsat TM data. The neural networks were found to be particularly suited to the application. A basic multi‐layer perceptron network, for example, provided estimates of biomass that were strongly correlated with those measured in the field (r = 0.80). Moreover, these estimates were more strongly correlated with biomass than those derived from 230 conventional vegetation indices, including the widely used normalized difference vegetation index (NDVI).  相似文献   

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环境灾害遥感小卫星在辽河三角洲湿地景观制图中的应用   总被引:2,自引:1,他引:2  
及时、准确地获得湿地的空间分布,对湿地的动态监测、保护与可持续利用具有重要的意义.环境灾害遥感小卫星星座A、B星(HJ-1A/1B星)是我国自主发射的陆地资源监测卫星,可为湿地类型的提取提供新的遥感影像数据源.本文通过对比我国环境灾害遥感小卫星CCD相机影像(HJ CCD)数据与Landsat TM5影像数据获取的湿地景观类型图的分类精度和各景观类型面积,验证和探究了HJ CCD数据在湿地景观动态变化监测中的适用性和应用潜力.结果表明:HJ CCD数据在地物识别分类方面可完全替代Landsat TM5数据;在实时动态监测方面,HJ CCD数据获取周期仅为2 d,优于Landsat TM5数据(16 d).  相似文献   

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Question: How does a newly designed method of supervised clustering perform in the assignment of relevé (species composition) data to a previously established classification. How do the results compare to the assignment by experts and to the assignment using a completely different numerical method? Material: Relevés analysed represent 4186 Czech grassland plots and 4990 plots from a wide variety of vegetation types (359 different associations or basal communities) in The Netherlands. For both data sets we had at our disposal an expert classification, and for the Czech data we also had available a numerical classification as well as a classification based on a neural network method (multi‐layer perceptron). Methods: Two distance indices, one qualitative and one quantitative, are combined into a single index by weighted multiplication. The composite index is a distance index for the dissimilarity between relevés and vegetation types. For both data sets the classifications by the new method were compared with the existing classifications. Results: For the Czech grasslands we correctly classified 81% of the plots to the classes of an expert classification at the alliance level and 71% to the classes of the numerical classification. Correct classification rates for the Dutch relevés were 64, 78 and 83 % for the lowest (subassociation or association), association, and alliance level, respectively. Conclusion: Our method performs well in assigning community composition records to previously established classes. Its performance is comparable to the performance of other methods of supervised clustering. Compared with a multi‐layer perceptron (a type of artificial neural network), fewer parameters have to be estimated. Our method does not need the original relevé data for the types, but uses synoptic tables. Another practical advantage is the provision of directly interpretable information on the contributions of separate species to the result.  相似文献   

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