Mapping of forest types in Alaskan boreal forests using SAR imagery

Rignot, Eric; Williams, C.; Way, J.; Viereck, Leslie A.

doi:10.1109/36.312893

Cited by 79 publications

(45 citation statements)

References 23 publications

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“…Study areas, the species investigated, and the number of training and validation samples differed substantially between the studies. Nevertheless, our achieved accuracies were comparable to other studies classifying deciduous and coniferous forests using even fully polarimetric C-band SAR data [16,83,84]. Compared with other technologies such as airborne laser scanning (OA of 89-96% and κ = 0.61 − 0.92) [85][86][87] or imaging spectrometer data (OA of 83-99% and κ = 0.73 − 0.98) [88][89][90], our forest type classification performance (OA of 86% and κ = 0.73) was not as competitive.…”

Section: Classification Of Forest Types and Speciessupporting

confidence: 74%

Using Multitemporal Sentinel-1 C-band Backscatter to Monitor Phenology and Classify Deciduous and Coniferous Forests in Northern Switzerland

2017

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Efficient methods to monitor forested areas help us to better understand their processes. To date, only a few studies have assessed the usability of multitemporal synthetic aperture radar (SAR) datasets in this context. Here we present an analysis of an unprecedented set of C-band observations of mixed temperate forests. We demonstrate the potential of using multitemporal C-band VV and VH polarisation data for monitoring phenology and classifying forests in northern Switzerland. Each SAR acquisition was first radiometrically terrain corrected using digital elevation model-based image simulations of the local illuminated area. The flattened backscatter values and the local area values were input to a temporal compositing process integrating backscatter values from ascending and descending tracks. The process used local resolution weighting of each input, producing composite backscatter values that strongly mitigated terrain-induced distortions. Several descriptors were calculated to show the seasonal variation of European beech (Fagus sylvatica), oak (Quercus robur, Quercus petraea) and Norway spruce (Picea abies) in C-band data. Using their distinct seasonal signatures, the timing of leaf emergence and leaf fall of the deciduous species were estimated and compared to available ground observations. Furthermore, classifications for the forest types 'deciduous' and 'coniferous' and the investigated species were implemented using random forest classifiers. The deciduous species backscatter was about 1 dB higher than spruce throughout the year in both polarisations. The forest types showed opposing seasonal backscatter behaviours. At VH, deciduous species showed higher backscatter in winter than in summer, whereas spruce showed higher backscatter in summer than in winter. In VV, this pattern was similar for spruce, while no distinct seasonal behaviour was apparent for the deciduous species. The time differences between the estimations and the ground observations of the phenological events were approximately within the error margin (±12 days) of the temporal resolution. The classification performances were promising, with higher accuracies achieved for the forest types (OA of 86% and κ = 0.73) than for individual species (OA of 72% and κ = 0.58). These results show that multitemporal C-band backscatter data have significant potential to supplement optical remote sensing data for ecological studies and mapping of mixed temperate forests.

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Section: Classification Of Forest Types and Speciessupporting

confidence: 74%

Using Multitemporal Sentinel-1 C-band Backscatter to Monitor Phenology and Classify Deciduous and Coniferous Forests in Northern Switzerland

2017

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“…To use SAR data for large-area forest mapping purposes, the sensitivity of the measurements to environmental and weather effects, such as precipitation and the associated canopy and soil moisture variations or freeze/thaw transitions, need to be accounted for [27,29,[62][63][64][65][66][67][68][69][70][71][72][73]. Ideally, models relating radar measurements to the forest biophysical attribute of interest are calibrated adaptively to account for temporal and spatial variations in the imaging conditions [15,18,19].…”

Section: Spatial Datasetsmentioning

confidence: 99%

A National, Detailed Map of Forest Aboveground Carbon Stocks in Mexico

et al. 2014

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“…Then for each pixel χ, we define the non-empty sets of the mass functions based on the statistical parameters above, as expressed in (8) …”

Section: High-level Fusion Using Dempster-shafer Evidence Theorymentioning

confidence: 99%

“…SAR as an active sensor offers all-weather, day/night coverage imaging capability, and it can also yield information on the underlying structure of land cover, particularly, woodland and grassland. Many studies have been conducted using SAR techniques, e.g., woodland classification, extraction and mapping [3][4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Woodland Extraction from High-Resolution CASMSAR Data Based on Dempster-Shafer Evidence Theory Fusion

Xie

Zhang

et al. 2015

Remote Sensing

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Mapping and monitoring of woodland resources is necessary, since woodland is vital for the natural environment and human survival. The intent of this paper is to propose a fusion scheme for woodland extraction with different frequency (P-and X-band) polarimetric synthetic aperture radar (PolSAR) and interferometric SAR (InSAR) data. In the study area of Hanjietou, China, a supervised complex Wishart classifier based on the initial polarimetric feature analysis was first applied to the PolSAR data and achieved an overall accuracy of 88%. An unsupervised classification based on elevation threshold segmentation was then applied to the InSAR data, with an overall accuracy of 90%. After Dempster-Shafer (D-S) evidence theory fusion processing for the PolSAR and InSAR classification results, the overall accuracy of fusion result reached 95%. It was found the proposed fusion method facilitates the reduction of polarimetric and interferometric SAR classification errors, and is suitable for the extraction of large areas of land cover with a uniform texture and height. The woodland extraction accuracy of the study area was sufficiently high (producer's accuracy of 96% and user's accuracy of 96%) enough that the woodland map generated from the fusion result can meet the demands of forest resource mapping and monitoring.

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Mapping of forest types in Alaskan boreal forests using SAR imagery

Cited by 79 publications

References 23 publications

Using Multitemporal Sentinel-1 C-band Backscatter to Monitor Phenology and Classify Deciduous and Coniferous Forests in Northern Switzerland

Using Multitemporal Sentinel-1 C-band Backscatter to Monitor Phenology and Classify Deciduous and Coniferous Forests in Northern Switzerland

A National, Detailed Map of Forest Aboveground Carbon Stocks in Mexico

Woodland Extraction from High-Resolution CASMSAR Data Based on Dempster-Shafer Evidence Theory Fusion

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