For remote communities in the discontinuous permafrost zone, access to permafrost distribution maps for hazard assessment is limited and more general products are often inadequate for use in local-scale planning. In this study we apply established analytical methods to illustrate a time- and cost-efficient method for conducting community-scale permafrost mapping in the community of Whati, Northwest Territories, Canada. We ran a binary logistic regression (BLR) using a combination of field data, digital surface model-derived variables, and remotely sensed products. Independent variables included vegetation, topographic position index, and elevation bands. The dependent variable was sourced from 139 physical checks of near-surface permafrost presence/absence sampled across the variable boreal-wetland environment. Vegetation is the strongest predictor of near-surface permafrost in the regression. The regression predicts that 50.0% (minimum confidence: 36%) of the vegetated area is underlain by near-surface permafrost with a spatial accuracy of 72.8%. Analysis of data recorded across various burnt and not-burnt environments indicated that recent burn scenarios have significantly influenced the distribution of near-surface permafrost in the community. A spatial burn analysis predicted up to an 18.3% reduction in near-surface permafrost coverage, in a maximum burn scenario without factoring in the influence of climate change. The study highlights the potential that in an ecosystem with virtually homogeneous air temperature, ecosystem structure and disturbance history drive short-term changes in permafrost distribution and evolution. Thus, at the community level these factors should be considered as seriously as changes to air temperature as climate changes.

The most dramatic permafrost degradation is expected to occur at the southern edge of permafrost distribution, which is difficult to detect directly on a large scale. Ecological indicators can be used to provide an early signal of changes in terrestrial ecosystems for regional near-surface permafrost habitats and potentially to monitor near-surface permafrost degradation. In this study, plant composition and community structure indicate the near-surface permafrost distribution at the southern edge of the boreal forest and permafrost in northeastern China. The plant species composition and structure of aboveground vegetation were linked to the belowground near-surface permafrost distribution in order to find indicators of changes in vegetation features from permafrost melting. These indicators are essential for assessing changes in permafrost vegetation systems under climate change. Carex schmidtii and C. appendiculata in the herb layer and Benda fruticosa in the shrub layer were found to be specific near-surface permafrost plant indicator species, especially for the wetland permafrost. Shrub cover, moss mat thickness and tree canopy cover are also strongly correlated with near-surface permafrost distribution. The active layer thickness (ALT) showed negative correlations with moss thickness and shrub cover because these features may act as buffers for regional climate warming. We chose the cover of each indicator species, near-surface permafrost-specific community features and geographical information as independent variables to predict the possible distribution of near-surface permafrost in our study region using logistic regression. The results showed that the prediction model had good performance and accuracy. Our study sheds light on early caution of deepening of regional-scale permafrost active layer with vegetation indicators that can further be identified from satellite images.

The Qinghai-Tibet Plateau (QTP), where is underlain by the highest and most extensive mid-altitude permafrost, is undergoing more dramatic climatic warming than its surrounding regions. Mapping the distribution of permafrost is of great importance to assess the impacts of permafrost changes on the regional climate system. In this study, we applied logistic regression model (LRM) andmulti-criteria analysis (MCA) methods to map the decadal permafrost distribution on the QTP and to assess permafrost dynamics from the 1980s to 2000s. The occurrence of permafrost and its impacting factors (i.e., climatic and topographic elements) were constructed from in-situ field investigation-derived permafrost distribution patterns in 4 selected study regions. The validation results indicate that both LRM and MCA could efficiently map the permafrost distribution on the QTP. The areas of permafrost simulated by LRM and MCA are 1.23 x 10(6) km(2) and 1.20 x 10(6) km(2), respectively, between 2008 and 2012. The LRM and MCA modeling results revealed that permafrost area has significantly decreased at a rate of 0.066 x 10(6) km(2) decade(-1) over the past 30 years, and the decrease of permafrost area is accelerating. The sensitivity test results indicated that LRM did well in identifying the spatial distribution of permafrost and seasonally frozen ground, and MCA did well in reflecting permafrost dynamics. More parameters such as vegetation, soil property, and soil moisture are suggested to be integrated into the models to enhance the performance of both models. (C) 2018 Published by Elsevier B.V.