Search

Microwave imagery has a distinct advantage over optical imagery in high-latitude areas because it allows data to be acquired independently of cloud cover and solar illumination. Synthetic aperture radar (SAR)-based monitoring has become increasingly important for understanding the state and dynamics of permafrost landscapes at the regional scale. This study presents a permafrost landscape mapping method that uses multi-temporal TerraSAR-X backscatter intensity and interferometric coherence information. The proposed method can classify permafrost landscape features and map the two most important features in sub-arctic permafrost environments: permafrost-affected areas and thermokarst ponds. First, a land cover map is generated through the combined use of object-based image analysis (OBIA) and classification and regression tree (CART) analysis. An overall accuracy of 98% is achieved when classifying rock and water bodies, and an accuracy of 79% is achieved when discriminating between different vegetation types with one year of single-polarized acquisitions. Second, the distributions of the permafrost-affected areas and thermokarst ponds are derived from the classified landscapes. Permafrost-affected areas are inferred from the relationship between vegetation cover and the existence of permafrost, and thermokarst pond distributions are directly inherited from the land cover map. The two mapped features exhibit good agreement with manually delineated references. The proposed method can produce permafrost landscape maps in complex sub-arctic environments and improve our understanding of the effects of climate change on permafrost landscapes. This classification strategy can be transferred to other time-series SAR datasets, e.g., Sentinel-1, and other heterogeneous environments. [ABSTRACT FROM AUTHOR]

South Patagonian peat bogs are little studied sources of methane (CH4). Since CH4 fluxes can vary greatly on a small scale of meters, high-quality maps are needed to accurately quantify CH4 fluxes from bogs. We used high-resolution color infrared (CIR) images captured by an Unmanned Aerial System (UAS) to investigate potential uncertainties in total ecosystem CH4 fluxes introduced by the classification of the surface area. An object-based approach was used to classify vegetation both on species and microform level. We achieved an overall Kappa Index of Agreement (KIA) of 0.90 for the species- and 0.83 for the microform-level classification, respectively. CH4 fluxes were determined by closed chamber measurements on four predominant microforms of the studied bog. Both classification approaches were employed to up-scale CH4 closed chamber measurements in a total area of around 1.8 hectares. Including proportions of the surface area where no chamber measurements were conducted, we estimated a potential uncertainty in ecosystem CH4 fluxes introduced by the classification of the surface area. This potential uncertainty ranged from 14.2 mg·m-2· day-1 to 26.8 mg·m-2·day-1. Our results show that a simple classification with only few classes potentially leads to pronounced bias in total ecosystem CH4 fluxes when plot-scale fluxes are up-scaled. [ABSTRACT FROM AUTHOR]

Manual vectorization of multispectral images is a widely used method for making land-use or land-cover maps. Although it is usually considered relatively accurate it is very time consuming, which has prompted the use in recent years of various semiautomatic methods for classifying remotely sensed images. One of the most promising of the latter is object-oriented image analysis based upon image segmentation, but the accuracy of its results, as well as its time demands, are disputed. Accordingly, this paper compared manual vectorization with object-oriented classification to reveal the strong and weak points of each. Two qualitatively different datasets were classified using both methods; time costs were monitored and accuracy levels were compared. It was found that manual vectorization achieved better overall accuracy (up to 93% versus 84%), but the semiautomatic method was usually more accurate when classifying some specific features such as roads, built-up areas, broadleaf trees and coniferous trees. The verdict regarding time-efficiency was less clear cut. The best method depends upon the spatial and spectral resolution of the data being processed. [ABSTRACT FROM AUTHOR]

The availability of multispectral images, with both high spatial and spectral resolution, makes it possible to obtain valuable information about complex urban environment, reducing the need for more expensive surveying techniques. Here, a methodology is tested for the semi-automatic extraction of buildings and the mapping of the main roofing materials over a urban area of approximately 100 km2, including the entire city of Bologna (Italy). The methodology follows an object-oriented approach and exploits a limited number of training samples. After a validation based on field inspections and close-range photos acquired by a drone, the final map achieved an overall accuracy of 94% (producer accuracy 79%) regarding the building extraction and of 91% for the classification of the roofing materials. The proposed approach proved to be flexible enough to catch the strong variability of the urban texture in different districts and can be easily reproducible in other contexts, as only satellite imagery is required for the mapping. [ABSTRACT FROM AUTHOR]