# Remote Sensing of Environmental Changes in Cold Regions: Methods, Achievements and Challenges
## 2 Principles and Methods
### Remote Sensing of Terrestrial Ecosystems
#### 2.5.1 Vegetation Mapping  
Land cover type is a general term encompassing a range of important information about ecosystems, including biotic and abiotic properties related to vegetation, energy balance, and carbon exchanges. Satellite remote sensing has been used throughout the cold regions to provide various maps of land cover that group vegetation according to geobotanical themes, including physiology. The spatial patterns in the vegetation maps can provide useful insight into how microclimates, soil type, and hydrology, disturbance and plant succession contribute to variations in plant community characteristics at landscape to regional levels. These maps are also used to parameterize ecosystem process models by means of parameter look-up tables aggregated according to generalized plant functional types [198; 199].  
Classification algorithms trained on satellite optical-IR spectral information have been used to map land cover and vegetation type over large areas. For example, the United States Geological Survey (USGS) provides 30-m land cover data for the state of Alaska for years 2001 and 2011, using the C5 decision-tree classifier applied to Landsat TM and ETM+ (Enhanced Thematic Mapper) imagery ([[http://www.mrlc.gov](http://www.mrlc.gov)]([http://www.mrlc.gov](http://www.mrlc.gov))). For the Anderson Level II classification (19 classes, defining different types of forest, shrub, herbaceous, and wetland), the overall accuracy ranged from 59% to 76%, depending on the definition of agreement with reference data [200]. The Earth Observation for Sustainable Development of Forests (EOSD) project used an unsupervised \(k\)-means clustering approach with Landsat ETM+ data to map land cover across Canada at 25-m resolution for the year 2000, using a detailed 23-class system [201]. In another study, phenological data derived from Landsat 8 NDVI timeseries, along with other inputs, were used to classify land cover across ice-free portions of Greenland at 30-m resolution [202]. Mapped classes included fen, dry heath and grasslands, wet heath, and copse and tall shrubs, with an overall classification accuracy of 89%. Other studies have used higher resolution imagery to map vegetation communities in heterogenous tundra landscapes (e.g., using IKONOS imagery [203]). For sites across the North Slope of Alaska, tundra vegetation communities were shown to be separable based on visible wavelengths collected through field spectroscopy, and vegetation type was mapped at 2-m resolution with \(\sim\)70% accuracy using linear discriminant analysis applied to WorldView-2 data [40].  
In addition to optical-IR data, airborne and satellite SAR data can be effective for separating land cover classes due to its sensitivity to vegetation structure and water content [204; 205; 206]. A supervised classification approach was applied to airborne, multifrequency polarimetric SAR imagery to map functional vegetation types at 30-m resolution across a boreal forest area [204]. Classes such as jack pine,black spruce, and trembling aspen were mapped with high accuracy (> 90%). Radar data can also be used to distinguish different types of wetland vegetation, as higher frequency microwave data can detect flooded short vegetation (e.g., tens and bogs), while lower frequency data are better at detecting flooded tall vegetation (e.g., forests and swamps) [206]. The fusion of microwave and optical data through traditional supervised classification (e.g., maximum-likelihood classifier) [205] and machine-learning approaches [206; 207] enables improved land cover classification over the high-latitudes. Deep-learning approaches also show promise in utilizing semantic information of satellite imagery for classifying complex ecosystems [208].