A decision-tree classification for low-lying complex land cover types within the zone of discontinuous permafrost

2014 ◽  
Vol 143 ◽  
pp. 73-84 ◽  
Author(s):  
L. Chasmer ◽  
C. Hopkinson ◽  
T. Veness ◽  
W. Quinton ◽  
J. Baltzer
2018 ◽  
Author(s):  
Kristine M. Haynes ◽  
Ryan F. Connon ◽  
William L. Quinton

Abstract. The discontinuous permafrost region of northwestern Canada is experiencing rapid warming resulting in dramatic land cover change from forested permafrost terrain to treeless wetlands. Extensive research has been conducted throughout this region to gain insight into how climate-induced land cover change will impact water resources and ecosystem function. This paper presents a hydrological and micrometeorological dataset collected in the Scotty Creek basin, Northwest Territories, Canada over the course of the Changing Cold Regions Network (CCRN) Special Observation and Analysis Period (SOAP) year of 1 October 2014 to 30 September 2015. Micrometeorological data collected from four stations located in land cover types representative of those comprising the Scotty Creek basin, including bog, channel fen, stable peat plateau and peat plateau undergoing rapid permafrost degradation and loss are presented. Monitored micrometeorological variables include incoming and outgoing shortwave and longwave radiation, air temperature, relative humidity, wind speed, precipitation (rain and snow) and snow depth. Deep ground temperatures (~ 1 to 10 m below the ground surface) from a channel fen as well as disturbed sites common to the basin including a seismic line and winter road are presented. Water levels were also monitored in the representative land cover types over this period. This dataset is available from the Wilfrid Laurier University Library Research Data Repository (https://doi.org/10.5683/SP/OQDRJG) and can be used in coordination with other hydrological and micrometeorological datasets, including those from the CCRN, to examine spatio-temporal effects of meteorological conditions on local hydrological responses across cold regions.


2008 ◽  
Vol 44 (2) ◽  
pp. 175-198 ◽  
Author(s):  
Eileen H. Helmer ◽  
Todd A. Kennaway ◽  
Diego H. Pedreros ◽  
Matthew L. Clark ◽  
Humfredo Marcano-Vega ◽  
...  

Author(s):  
J. Höhle

A 2D land cover map can automatically and efficiently be generated from high-resolution multispectral aerial images. First, a digital surface model is produced and each cell of the elevation model is then supplemented with attributes. A decision tree classification is applied to extract map objects like buildings, roads, grassland, trees, hedges, and walls from such an "intelligent" point cloud. The decision tree is derived from training areas which borders are digitized on top of a false-colour orthoimage. The produced 2D land cover map with six classes is then subsequently refined by using image analysis techniques. The proposed methodology is described step by step. The classification, assessment, and refinement is carried out by the open source software "R"; the generation of the dense and accurate digital surface model by the "Match-T DSM" program of the Trimble Company. A practical example of a 2D land cover map generation is carried out. Images of a multispectral medium-format aerial camera covering an urban area in Switzerland are used. The assessment of the produced land cover map is based on class-wise stratified sampling where reference values of samples are determined by means of stereo-observations of false-colour stereopairs. The stratified statistical assessment of the produced land cover map with six classes and based on 91 points per class reveals a high thematic accuracy for classes "building" (99 %, 95 % CI: 95 %-100 %) and "road and parking lot" (90 %, 95 % CI: 83 %-95 %). Some other accuracy measures (overall accuracy, kappa value) and their 95 % confidence intervals are derived as well. The proposed methodology has a high potential for automation and fast processing and may be applied to other scenes and sensors.


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