Land cover classification using multi-temporal MERIS vegetation indices

The spectral, spatial, and temporal resolutions of Envisat's Medium Resolution Imaging Spectrometer (MERIS) data are attractive for regional- to global-scale land cover mapping. Moreover, two novel and operational vegetation indices derived from MERIS data have considerable potential as discriminating variables in land cover classification. Here, the potential of these two vegetation indices (the MERIS global vegetation index (MGVI), MERIS terrestrial chlorophyll index (MTCI)) was evaluated for mapping eleven broad land cover classes in Wisconsin. Data acquired in the high and low chlorophyll seasons were used to increase inter-class separability. The two vegetation indices provided a higher degree of inter-class separability than data acquired in many of the individual MERIS spectral wavebands. The most accurate landcover map (73.2%) was derived from a classification of vegetation index-derived data with a support vector machine (SVM), and was more accurate than the corresponding map derived from a classification using the data acquired in the original spectral wavebands

Remote sensing/vegetation classification

The CALVEG classification system for identification of vegetation is described. This hierarchical system responds to classification requirements and to interpretation of vegetation at various description levels, from site description to broad identification levels. The system's major strength is its flexibility in application of remote sensing technology to assess, describe and communicate data relative to vegetative resources on a state-wide basis. It is concluded that multilevel remote sensing is a cost effective tool for assessment of the natural resource base. The CLAVEG system is found to be an economically efficient tool for both existing and potential vegetation

FOUR YEARS OF UNMANNED AERIAL SYSTEM IMAGERY REVEALS VEGETATION CHANGE IN A SUB-ARCTIC MIRE DUE TO PERMAFROST THAW

Warming trends in sub-arctic regions have resulted in thawing of permafrost which in turn induces change in vegetation across peatlands both in areal extent and composition. Collapse of palsas (i.e. permafrost plateaus) has also been correlated with increases in methane (CH4) emission to the atmosphere. Vegetation change provides new microenvironments that promote CH4 production and emission, specifically through plant interactions and structure. By quantifying the changes in vegetation at the landscape scale, we will be able to scale the impact of thaw on CH4 emissions in these complex climate-sensitive northern ecosystems. We combine field-based measurements of vegetation composition and Unmanned Aerial Systems (UAS) high resolution (3 cm) imagery to characterize vegetation change in a sub-arctic mire. The objective of this study is to analyze how vegetation from Stordalen Mire, Abisko, Sweden, has changed over time in response to permafrost thaw. At Stordalen Mire, we flew a fixed-wing UAS in July of each of four years, 2014 through 2017, over a 1 km x 0.5 km area. High precision GPS ground control points were used to georeference the imagery. Randomized square-meter plots were measured for vegetation composition and individually classified into one of five vegetation cover types, each representing a different stage of permafrost degradation. Using these training data, each year of imagery was classified by cover type in Google Earth Engine using a Random Forest Classifier. Textural information was extracted from the imagery, which provided additional spatial context information and improved classification accuracy. Twenty five percent of the training data were held back from the classification and used for validation, while the remaining seventy five percent of the training data were used to classify the imagery. The overall classification accuracy for 2014-2017 was 80.6%, 79.1%, 82.0%, and 82.9%, respectively. Percent cover across the landscape was calculated from each classification map and compared between years. Hummock sites, representing intact permafrost, decreased coverage by 9% from 2014-2017, while semi-wet sites increased coverage by 18%. This four-year comparison of vegetation cover indicated a rapid response to permafrost thaw. The use of a UAS allowed us to effectively capture the spatial heterogeneity of a northern peatland ecosystem. Estimation of vegetation cover types is vital in our understanding of the evolution of northern peatlands and their future role in the global carbon cycle

Historical Grassland Turboveg Database Project. 2067 Relevés recorded by Dr Austin O’ Sullivan 1962 – 1982

User Guide and CD of Database are availableEnd of project reportThe more common grassland types occupy about 70% of the Irish landscape (O’Sullivan, 1982), but information on these vegetation types is rare. Generally, Irish grasslands are distinguished based on the intensity of their management (improved or semi-natural grasslands), and the drainage conditions and acidity of the soil (dry or wet, calcareous or acidic grassland types) (Fossitt, 2000). However, little is known about their floristic composition and the changes in floristic composition over time. The current knowledge on grassland vegetation is mostly based on a survey of Irish grasslands by Dr. Austin O’Sullivan completed in the 1960’s and 1970’s (O’Sullivan, 1982). In this survey O’Sullivan identified Irish grassland types in accordance with the classification of continental European grasslands based on the principles of the School of Phytosociology. O’Sullivan distinguished five main grassland types introducing agricultural criteria as well as floristic criteria into grassland classification (O’Sullivan, 1982). In 1978, O’Sullivan made an attempt at mapping Ireland’s vegetation types including the five grassland types distinguished in his later publication as well as two types of peatland vegetation (Figures 1 and 2). This map was completed using 1960’s soils maps (National Soil Survey, Teagasc, Johnstown Castle) and a subsample of the dataset on the composition of Irish grasslands. Phytosociological classification of vegetation is based on the full floristic composition of the vegetation as determined by assessing the abundance and spatial structure of the plant species in a given area. The actual area of the survey (or relevé) is determined according to strict criteria, which include how representative the sample area is for the wider vegetation (i.e. how many of the species found in the wider area are also present in the survey area).National Parks and Wildlife Service of the Department of the Environment, Heritage and Local Government, Dublin, Ireland

Foreword

This issue of Cunninghamia contains the first two papers of a project involving the classification and assessment of the native vegetation of New South Wales, Australia (NSWVCA). Besides developing a comprehensive typology of the vegetation, the project aims to assess the protected area and threat status of the State’s vegetation. It collates information on vegetation composition, geographic distribution of plant communities, physiographic features, threats, aspects of condition, planning and management and representation in protected areas into a single database system. A photographic library is also being collated for use with the database and use in publications and education programs

Remote sensing of earth terrain

A systematic approach for the identification of terrain media such as vegetation canopy, forest, and snow covered fields is developed using the optimum polarimetric classifier. The covariance matrices for the various terrain cover are computed from theoretical models of random medium by evaluating the full polarimetric scattering matrix elements. The optimal classification scheme makes use of a quadratic distance measure and is applied to classify a vegetation canopy consisting of both trees and grass. Experimentally measured data are used to validate the classification scheme. Theoretical probability of classification error using the full polarimetric matrix are compared with classification based on single features including the phase difference between the VV and HH polarization returns. It is shown that the full polarimetric results are optimal and provide better classification performance than single feature measurements

Classification and Protection Status of Remnant Natural Plant Communities in Arkansas

A classification and inventory of Arkansas\u27s remaining tracts of relatively undisturbed vegetation was initiated in 1979. Based on extensive literature surveys and field work, the classification includes five physiognomic classes, 17 cover classes, and 46 cover types, arranged hierarchically. High quality examples of ten of the cover types have been located in designated wilderness or state natural areas, where they are protected by law, while an additional three occur in research natural areas or Forest Service special interest areas. The remaining 33 cover types have no known long-term protection. Lands having wilderness, state natural area, research natural area, or special management area status total nearly 51,000 acres in the state. No more than one-tenth of this area, however, supports vegetation in relatively undisturbed condition

A comparative framework for broad-scale plot-based vegetation classification

Aims: Classification of vegetation is an essential tool to describe, understand, predict and manage biodiversity. Given the multiplicity of approaches to classify vegetation, it is important to develop international consensus around a set of general guidelines and purpose-specific standard protocols. Before these goals can be achieved, however, it is necessary to identify and understand the different choices that are made during the process of classifying vegetation. This paper presents a framework to facilitate comparisons between broad-scale plot-based classification approaches. Results: Our framework is based on the distinction of four structural elements (plot record, vegetation type, consistent classification section and classification system) and two procedural elements (classification protocol and classification approach). For each element we describe essential properties that can be used for comparisons. We also review alternative choices regarding critical decisions of classification approaches; with a special focus on the procedures used to define vegetation types from plot records. We illustrate our comparative framework by applying it to different broad-scale classification approaches. Conclusions: Our framework will be useful for understanding and comparing plot-based vegetation classification approaches, as well as for integrating classification systems and their sections. We present a comparison framework for vegetation classification that distinguishes four structural elements (plot record, vegetation type, consistent classification section and classification system) and two procedural elements (classification protocol and classification approach). The framework will be useful for understanding and comparing plot-based vegetation classification approaches, as well as for integrating classification systems and their sections. © 2015 International Association for Vegetation Science

Adopting national vegetation guidelines and the National Vegetation Information System (NVIS) framework in the Northern Territory

Guidelines and core attributes for site-based vegetation surveying and mapping developed for the Northern Territory, are relevant to botanical research, forestry typing, rangeland monitoring and reporting on the extent and condition of native and non-native vegetated landscapes. These initiatives are consistent with national vegetation guidelines and the National Vegetation Information System (NVIS) framework. This paper provides a synopsis of vegetation site data collection, classification and mapping in the Northern Territory, and discusses the benefits of consistency between the guidelines, core attributes and the NVIS framework; both of which has an emphasis on the NVIS hierarchical classification system for describing structural and floristic attributes of vegetation. The long-term aim of the NVIS framework is that national attributes are adopted at regional levels to enable comparability of vegetation information within survey and jurisdictional boundaries in the Northern Territory and across Australia. The guidelines and core attributes are incorporated in current and future vegetation survey and mapping programs in the Northern Territory

Vegetation and environmental patterns on soils derived from Hawkesbury Sandstone and Narrabeen substrata in Ku-ring-gai Chase National Park, New South Wales

[Abstract]: The vegetation patterns in the Central Coast region of New South Wales have been extensively studied with respect to single environmental variables, particularly soil nutrients. However, few data are available on the effects of multiple environmental variables. This study examines the relationships between vegetation and multiple environmental variables in natural vegetation on two underlying rock types, Hawkesbury sandstone and Narrabeen group shales and sandstones, in Ku-ring-gai Chase National Park, Sydney. Floristic composition and 17 environmental factors were characterized using duplicate 500 m2 quadrats from fifty sites representing a wide range of vegetation types. The patterns in vegetation and environmental factors were examined through multivariate analyses: indicator species analysis was used to provide an objective classification of plant community types, and the relationships between vegetation and environmental factors within the two soil types were examined through indirect and direct gradient analyses. Eleven plant communities were identified, which showed strong agreement with previous studies. The measured environmental factors showed strong correlations with vegetation patterns: within both soil types, the measured environmental variables explained approximately 32 - 35% of the variation in vegetation. No single measured environmental variable adequately described the observed gradients in vegetation; rather, vegetation gradients showed strong correlations with complex environmental gradients. These complex environmental gradients included nutrient, moisture and soil physical and site variables. These results suggest a simple 'nutrient' hypothesis regarding vegetation patterns in the Central Coast region is inadequate to explain variation in vegetation within soil types