Linking vegetation heterogeneity and functional attributes of temperate grasslands through remote sensing

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.     

Satellite remote sensing of freshwater macrophytes and the influence of water clarity

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.     

Vegetation mapping of the Great Fish River basin,South Africa: Integrating spatial and multi‐spectral remote sensing techniques

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.     

Spatial variability of above‐ground net primary production in Uruguayan grasslands: a remote sensing approach

Question: How does above‐ground net primary production (ANPP) differ (estimated from remotely sensed data) among vegetation units in sub‐humid temperate grasslands? Location: Centre‐north Uruguay. Methods: A vegetation map of the study area was generated from LANDSAT imagery and the landscape configuration described. The functional heterogeneity of mapping units was analysed in terms of the fraction of photosynthetically active radiation absorbed by green vegetation (fPAR), calculated from the normalized difference vegetation index (NDVI) images provided by the moderate resolution imaging spectroradiometer (MODIS) sensor. Finally, the ANPP of each grassland class was estimated using NDVI and climatic data. Results: Supervised classification presented a good overall accuracy and moderate to good average accuracy for grassland classes. Meso‐xerophytic grasslands occupied 45% of the area, Meso‐hydrophytic grasslands 43% and Lithophytic steppes 6%. The landscape was shaped by a matrix of large, unfragmented patches of Meso‐xerophytic and Meso‐hydrophytic grasslands. The region presented the lowest anthropic fragmentation degree reported for the Rio de la Plata grasslands. All grassland units showed bimodal annual fPAR seasonality, with spring and autumn peaks. Meso‐hydrophytic grasslands showed a radiation interception 10% higher than the other units. On an annual basis, Meso‐hydrophytic grasslands produced 3800 kg dry matter (DM) ha^?1 yr^?1 and Meso‐xerophytic grasslands and Lithophytic steppes around 3400 kg·DM·ha^?1·yr^?1. Meso‐xerophytic grasslands had the largest spatial variation during most of the year. The ANPP temporal variation was higher than the fPAR variability. Conclusions: Our results provide valuable information for grazing management (identifying spatial and temporal variations of ANPP) and grassland conservation (identifying the spatial distribution of vegetation units).     

Native woody vegetation in central Argentina: Classification of Chaco and Espinal forests

Question

What are the composition and spatial patterns of native woody plant communities in the southern Great Chaco and Espinal?

Location

Córdoba Province, central Argentina, an area of ca. 161,000 km².

Methods

We collected 351 geo‐referenced relevés representative of the geographic, topographic and ecological variation of the Chaco and Espinal woody vegetation in central Argentina. The relevés were classified into vegetation types using the hierarchical ISOPAM method. Forest and shrubland types were described on the basis of diagnostic species occurrences and their distribution in relation to environmental factors. A map of the actual vegetation derived from remote‐sensed images (Landsat) and field data was used to describe the current distribution and abundance of the different vegetation types.

Results

The classification of the 351 plots × 837 species matrix revealed two major clusters comprising seven woody vegetation types corresponding to Chaco lowland and mountain forests and shrublands, Espinal forests and edaphic vegetation. The most important gradients in woody vegetation types are related to elevation, temperature and rainfall variables.

Conclusions

Subtropical seasonally dry woody plant communities from the southern extreme of the Great Chaco and Espinal forests were described for the first time based on complete floristic data. Our results show that lowland Chaco native forests, as well as replacement communities, are still present in its southern distribution range and are well distinguishable from other vegetation types such as the Espinal and mountain forests. Overall, extensive Espinal forests have almost disappeared while Chaco vegetation is highly fragmented and degraded.

     

A comparison of cover calculation techniques for relating point-intercept vegetation sampling to remote sensing imagery

Accurate and timely spatial predictions of vegetation cover from remote imagery are an important data source for natural resource management. High-quality in situ data are needed to develop and validate these products. Point-intercept sampling techniques are a common method for obtaining quantitative information on vegetation cover that have been widely implemented in a number of local and national monitoring programs. The use of point-intercept data in remote sensing projects, however, is complicated due to differences in how vegetation cover indicators can be calculated. Decisions on whether to use plant intercepts from any canopy layer (i.e., any-hit cover) or only the first plant intercept at each point (i.e., top-hit cover) can result in discrepancies in cover estimates which are used to train remotely-sensed imagery. Our objective in this paper was to explore the theory of point-intercept sampling relative to training and testing remotely-sensed imagery, and to test the strength of relationships between top-hit and any-hit methods of calculating vegetation cover and high-resolution satellite imagery in two study areas managed by the Bureau of Land Management in northwestern Colorado and northeastern California. We modeled top-hit and any-hit percent cover for six vegetation indicators from 5m-resolution RapidEye imagery using beta regression. Model performance was judged using normalized root mean-squared error (RMSE) from a 5-fold cross validation. Any-hit cover estimates were significantly higher (α < 0.05) than top-hit cover estimates for forbs and grasses in the White River study area, but only marginally higher in Northern California. Pseudo-R² values for beta regression models of vegetation cover from RapidEye image information varied from 0.1525 to 0.7732 in White River and 0.2455 to 0.6085 in Northern California, with little pattern to whether any-hit or top-hit indicators produced better model fit. However, normalized RMSE was lower for any-hit cover (indicating better model performance) or minimally higher than top-hit cover for all indicators in each study area. Our results do not support the idea that top-hit cover estimates from point-intercept sampling are the most appropriate for remote sensing applications in arid and semi-arid shrub-steppe environments. In fact, having two sets of different indicators calculated from the same data may cause additional confusion in a situation where there is already considerable debate on how vegetation cover should be measured and used. Ultimately, selection of indicators to use for developing remote sensing classification or predictive models should be based first on the meaning or interpretation of the indicator in the ecosystem of interest, and second on how well the indicator performs in modeling applications.     

Combining land cover mapping of coastal dunes with vegetation analysis

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.     

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

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）.     

Classifying the Neotropical savannas of Belize using remote sensing and ground survey

Aim This paper evaluates a method of combining data from GPS ground survey with classifications of medium spatial resolution LANDSAT imagery to distinguish variations within Neotropical savannas and to characterize the boundaries between savanna areas and the associated gallery forests, seasonally dry forests and wetland communities. Location Rio Bravo Conservation Area, Orange Walk District, Belize, Central America. Methods Dry season LANDSAT data for 10 April 1993 and 9 March 2001 covering a conservation area of 240,000 acres (97,459 ha), were rectified to sub‐pixel accuracy using ground control points positioned by GPS ground survey. The 1993 image was used to assess the accuracy with which the boundaries between the savanna matrix and gallery forests, high forests, wetlands and water bodies could be discriminated. The image was classified by a maximum likelihood (ML) classifier and the shapes and areas of forest and wetland classes were compared with an interpretation of these land cover types from 1 : 24,000 aerial photography, mapped at 1 : 50,000 scale in 1993. The 2001 image was used to assess whether different subtypes of savanna could be distinguished from LANDSAT data. This required the creation of a reference (‘ground truth’) data set for testing classifications of the image. One hundred and sixty sample patches (650 ha, distributed over an area of 7000 ha) of ten sub‐types of savanna vegetation and associates identified using a physiognomic classification scheme, were delineated on the ground by GPS and divided into two subsets for training and testing. Continuous classifications of LANDSAT data covering the savannas were developed that estimated potential contributions from up to five sub‐types of land cover (grassland, wetland, pine woodland, gallery forest and palmetto). The accuracy of each classification was assessed by comparison against ground data. An ML classification was also produced for the 2001 image using the same areas for training. This allowed a comparison of the relative accuracy of both continuous and Boolean ML methods for classifying savanna areas. Results The boundary between savannas and evergreen forests, gallery forests and open water in the study region could be delineated by the ML classifier to within 2 pixels (60 m) using LANDSAT imagery. However, the constituent sub‐types within the savanna were poorly discriminated. Whilst the shape and extent of closed canopy forest, gallery forest, wetlands and water bodies agreed closely with the distributions interpreted from aerial photography, classes such as ‘open pine savanna’ or ‘grassland’ were only 45–65% accurate when tested against ground data. A continuous classification, estimating the proportions of three savanna vegetation subtypes (grassland, marshland and woodland) present in each pixel, correctly classified more of the ground data for these cover types than the comparable ML result. Proportional mixtures of the land cover estimated by the continuous classifier also compared realistically with the vegetation formations observed along ground transects. Main conclusions By using GPS, a ground survey of vegetation cover was accurately matched to remotely sensed imagery and the accuracy of delineating boundaries and classifying areas of savanna was assessed directly. This showed that ML classification techniques can reliably delineate the boundaries of savannas, but continuous classifiers more accurately and realistically represent the distribution of the subtypes comprising savanna land cover. By combining these ground survey and image classification methods, medium spatial resolution satellite sensor data can provide an affordable means for land managers to assess the nature, extent and distribution of savanna formations. Over time, using the archives of LANDSAT (and SPOT) data together with marker sites surveyed in the field, quantitative changes in the extents and boundaries of savannas in response to both natural (e.g. fire, hurricane and drought) and anthropogenic (e.g. cutting and disturbance) factors can be assessed.     

Characterization of moorland vegetation and the prediction of bird abundance using remote sensing

Aims To characterize and identify upland vegetation composition and height from a satellite image, and assess whether the resulting vegetation maps are accurate enough for predictions of bird abundance. Location South‐east Scotland, UK. Methods Fine‐taxa vegetation data collected using point samples were used for a supervised classification of a Landsat 7 image, while linear regression was used to model vegetation height over the same image. Generalized linear models describing bird abundance were developed using field‐collected bird and vegetation data. The satellite‐derived vegetation data were substituted into these models and efficacy was examined. Results The accuracy of the classification was tested over both the training and a set of test plots, and showed that more common vegetation types could be predicted accurately. Attempts to estimate the heights of both dwarf shrub and graminoid vegetation from satellite data produced significant, but weak, correlations between observed and predicted height. When these outputs were used in bird abundance–habitat models, bird abundance predicted using satellite‐derived vegetation data was very similar to that obtained when the field‐collected data were used for one bird species, but poor estimates of vegetation height produced from the satellite data resulted in a poor abundance prediction for another. Conclusions This pilot study suggests that it is possible to identify moorland vegetation to a fine‐taxa level using point samples, and that it may be possible to derive information on vegetation height, although more appropriate field‐collected data are needed to examine this further. While remote sensing may have limitations compared with relatively fine‐scale fieldwork, when used at relatively large scales and in conjunction with robust bird abundance–habitat association models, it may facilitate the mapping of moorland bird abundance across large areas.     

A vegetation map of The Netherlands,based on the relationship between ecotopes and types of potential natural vegetation

Summary The method of mapping the vegetation on scale 1: 200,000 and the starting points in relation to the potential natural vegetation and ecotopes, are discussed.In view of the planological background of this study, some restrictions have been added to the concept of potential natural vegetation, concerning the period of development and the human influence.The relationship between soil, ground water and vegetation was studied, which resulted in the map of the potential natural vegetation.Each type of potential natural vegetation stands for a series of vegetation types on the same site. Seven main series, with a number of sub-series are distinguished. Within each vegetation series the plant communities have been spread over five groups, according to their structure and naturalness.Ecotopes and ecotope complexes are considered as landscape ecological units. A list of ecotopes was obtained by interpreting topographical maps and by inventory data.The actual vegetation was mapped by estimating the size of the ecotopes within the separate areas. It was expressed in a five figure code for the five groups from the vegetation and ecotopes is combined into the vegetation map of The Netherlands.Interpretation problems, some of them specific for The Netherlands, are discussed and some remarks are made on the necessity of further research.Contribution to the Symposium on Plant Species and Plant Communities, held at Nijmegen, 11–12 November 1976, on the occasion of the 60th birthday of Professor Victor Westhoff.Nomenclature follows Heukels-van Ooststroom, Flora van Nederland, 18e druk, 1975, Wolters-Noordhoff, Groningen; nomenclature of syntaxa follows Westhoff & den Held (1969)     

Assessing the effect of EWDP on vegetation restoration by remote sensing in the lower reaches of Tarim River

In this study, effect of ecological water diversion on vegetation restoration in the lower reaches of Tarim River is assessed by coupling remote sensing techniques and a field-based survey. Land use/cover and fractional vegetation coverage (Fvc) maps derived from remote sensing images, ground validation data, and hydrological observation data are adopted to analyze the responses of Ecological Water Diversion Project (EWDP). The results indicate that, the EWDP has showed a positive effect on vegetation restoration in the lower part of Tarim Basin. During 2001 to 2013, transformation from unused land to nature vegetation (i.e. forest land, grassland and scrubland) was the major process of land use/cover change; the area of natural vegetation showed a 4.7% increase, and the area of unused land reduced by 6.8%. Landscape patch size was decreased, the degree of fragmentation and diversity of landscape was increased, and landscape structure in the study area became more complex. Moreover, vegetation coverage promoted from 2001 to 2013; average Fvc in 2013 was 1.5 times greater than that in 2001. The results can provide not only an accurate assessment for the EWDP, but also a visual insight for the water resources management practices in the study area, such that the sustainability for local ecosystem can be facilitated.