Statistical Design and Analysis in Long-Term Vegetation Monitoring

2001 ◽  
pp. 17-39 ◽  
Author(s):  
Otto Wildi
Keyword(s):  
Statistical Design ◽  
Vegetation Monitoring

scholarly journals Consistent Long-Term Monthly Coastal Wetland Vegetation Monitoring Using a Virtual Satellite Constellation

2021 ◽  
Vol 13 (3) ◽  
pp. 438
Author(s):  
Subrina Tahsin ◽  
Stephen C. Medeiros ◽  
Arvind Singh
Keyword(s):  
Vegetation Index ◽  
Coastal Wetland ◽  
Thermal Emission ◽  
Mean Squared Error ◽  
Wetland Vegetation ◽  
Coefficient Of Determination ◽  
Landsat 8 ◽  
Vegetation Monitoring ◽  
Time Space

Long-term monthly coastal wetland vegetation monitoring is the key to quantifying the effects of natural and anthropogenic events, such as severe storms, as well as assessing restoration efforts. Remote sensing data products such as Normalized Difference Vegetation Index (NDVI), alongside emerging data analysis techniques, have enabled broader investigations into their dynamics at monthly to decadal time scales. However, NDVI data suffer from cloud contamination making periods within the time series sparse and often unusable during meteorologically active seasons. This paper proposes a virtual constellation for NDVI consisting of the red and near-infrared bands of Landsat 8 Operational Land Imager, Sentinel-2A Multi-Spectral Instrument, and Advanced Spaceborne Thermal Emission and Reflection Radiometer. The virtual constellation uses time-space-spectrum relationships from 2014 to 2018 and a random forest to produce synthetic NDVI imagery rectified to Landsat 8 format. Over the sample coverage area near Apalachicola, Florida, USA, the synthetic NDVI showed good visual coherence with observed Landsat 8 NDVI. Comparisons between the synthetic and observed NDVI showed Root Mean Squared Error and Coefficient of Determination (R2) values of 0.0020 sr−1 and 0.88, respectively. The results suggest that the virtual constellation was able to mitigate NDVI data loss due to clouds and may have the potential to do the same for other data. The ability to participate in a virtual constellation for a useful end product such as NDVI adds value to existing satellite missions and provides economic justification for future projects.

scholarly journals What is the most efficient and effective method for long-term monitoring of alpine tundra vegetation?

2016 ◽  
Vol 2 (3) ◽  
pp. 127-141 ◽  
Author(s):  
Steven D. Mamet ◽  
Nathan Young ◽  
Kwok P. Chun ◽  
Jill F. Johnstone
Keyword(s):  
Species Richness ◽  
Relative Abundance ◽  
Rare Species ◽  
Alpine Tundra ◽  
The Arctic ◽  
Vegetation Monitoring ◽  
Tundra Vegetation ◽  
Long Term Monitoring ◽  
Term Monitoring

Nondestructive estimations of plant community characteristics are essential to vegetation monitoring programs. However, there is no universally accepted method for this purpose in the Arctic, partly because not all programs share the same logistical constraints and monitoring goals. Our aim was to determine the most efficient and effective method for long-term monitoring of alpine tundra vegetation. To achieve this, we established 12 vegetation-monitoring plots on a south-facing slope in the alpine tundra of southern Yukon Territory, Canada. Four observers assessed these plots for vascular plant species abundance employing three methods: visual cover (VC) and subplot frequency (SF) estimation and modified point-intercept (PI) (includes rare species present but not intersected by a pin). SF performed best in terms of time required per plot and sensitivity to variations in species richness. All methods were similarly poor at estimating relative abundance for rare species, but PI and VC were substantially better at high abundances. Differences among methods were larger than among observers. Our results suggest that SF is best when the monitoring focus is on rare species or species richness across extensive areas. However, when the focus is on monitoring changes in relative abundance of common species, VC or PI should be preferred.

scholarly journals Long-term vegetation monitoring in Great Britain – the Countryside Survey 1978–2007 and beyond

2017 ◽  
Vol 9 (2) ◽  
pp. 445-459 ◽  
Author(s):  
Claire M. Wood ◽  
Simon M. Smart ◽  
Robert G. H. Bunce ◽  
Lisa R. Norton ◽  
Lindsay C. Maskell ◽  
...  
Keyword(s):  
Great Britain ◽  
Land Cover ◽  
Survey Design ◽  
Ecological Data ◽  
Vegetation Monitoring ◽  
Environmental Classification ◽  
Freshwater Habitats ◽  
Vegetation Surveys

Abstract. The Countryside Survey (CS) of Great Britain provides a globally unique series of datasets, consisting of an extensive set of repeated ecological measurements at a national scale, covering a time span of 29 years. CS was first undertaken in 1978 to monitor ecological and land use change in Britain using standardised procedures for recording ecological data from representative 1 km squares throughout the country. The same sites, with some additional squares, were used for subsequent surveys of vegetation undertaken in 1990, 1998 and 2007, with the intention of future surveys. Other data records include soils, freshwater habitats and invertebrates, and land cover and landscape feature diversity and extents. These data have been recorded in the same locations on analogous dates. However, the present paper describes only the details of the vegetation surveys. The survey design is a series of gridded, stratified, randomly selected 1 km squares taken as representative of classes derived from a statistical environmental classification of Britain. In the 1978 survey, 256 one-kilometre sample squares were recorded, increasing to 506 in 1990, 569 in 1998 and 591 in 2007. Initially each square contained up to 11 dispersed vegetation plots but additional plots were later placed in different features so that eventually up to 36 additional sampling plots were recorded, all of which can be relocated where possible (unless the plot has been lost, for example as a consequence of building work), providing a total of 16 992 plots by 2007. Plots are estimated to have a precise relocation accuracy of 85 %. A range of plots located in different land cover types and landscape features (for example, field boundaries) are included. Although a range of analyses have already been carried out, with changes in the vegetation being related to a range of drivers at local and national scales, there is major potential for further analyses, for example in relation to climate change. Although the precise locations of the plots are restricted, largely for reasons of landowner confidentiality, sample sites are intended to be representative of larger areas, and many potential opportunities for further analyses remain. Data from each of the survey years (1978, 1990, 1998, 2007) are available via the following DOIs: Countryside Survey 1978 vegetation plot data (https://doi.org/10.5285/67bbfabb-d981-4ced-b7e7-225205de9c96), Countryside Survey 1990 vegetation plot data (https://doi.org/10.5285/26e79792-5ffc-4116-9ac7-72193dd7f191), Countryside Survey 1998 vegetation plot data (https://doi.org/10.5285/07896bb2-7078-468c-b56d-fb8b41d47065), Countryside Survey 2007 vegetation plot data (https://doi.org/10.5285/57f97915-8ff1-473b-8c77-2564cbd747bc).

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