Abstract. Boreal fire regimes are intensifying because of climate change and the northern parts of boreal forests are underlain by permafrost. Boreal fires combust vegetation and organic soils, which insulate permafrost, and as such deepen the seasonally thawed active layer and can lead to further carbon emissions to the atmosphere. Current understanding of the environmental drivers of post-fire thaw depth is limited but of critical importance. In addition, mapping thaw depth over fire scars may enable a better understanding of the spatial variability in post-fire responses of permafrost soils. We assessed the environmental drivers of post-fire thaw depth using field data from a fire scar in a larch-dominated forest in the continuous permafrost zone in Eastern Siberia. Particularly, summer thaw depth was deeper in burned (mean = 127.3 cm, standard deviation (sd) = 27.7 cm) than in unburned (98.1 cm, sd = 26.9 cm) landscapes one year after the fire, yet the effect of fire was modulated by landscape and vegetation characteristics. We found deeper thaw in well-drained landscape positions, in open larch forest often intermixed with Scots pine, and in high severity burns. The environmental drivers, site moisture, forest type and density, and fire severity explained 73.4 % of the measured thaw depth variability at the study sites. In addition, we evaluated the relationships between field-measured thaw depth and several remote sensing proxies. Albedo, the differenced Normalized Burn Ratio (dNBR), land surface temperature (LST), and pre-fire Normalized Difference Vegetation Index (NDVI) derived from Landsat 8 imagery together explained 66.3 % of the variability in field-measured thaw depth. Based on these remote sensing proxies and multiple linear regression analysis, we estimated thaw depth over the entire fire scar, and found that LST displayed particularly strong correlations with post-fire thaw depth (r = 0.65, p < 0.01). Our study reveals some of the governing processes of post-fire thaw depth development and shows the capability of Landsat imagery to estimate thaw depth at a landscape scale.
", "keywords": ["Dynamic and structural geology", "QE1-996.5", "13. Climate action", "Science", "Q", "Geology", "QE500-639.5", "Deforestation", "15. Life on land", "Landsat", "Multiple linear regression", "Atmospheric temperature"]}, "links": [{"href": "https://esd.copernicus.org/articles/15/1459/2024/esd-15-1459-2024.pdf"}, {"href": "https://doi.org/1871.1/0b041c5c-edd1-45f1-895d-546207d34a0a"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Earth%20System%20Dynamics", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "1871.1/0b041c5c-edd1-45f1-895d-546207d34a0a", "name": "item", "description": "1871.1/0b041c5c-edd1-45f1-895d-546207d34a0a", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/1871.1/0b041c5c-edd1-45f1-895d-546207d34a0a"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-03-21T00:00:00Z"}}, {"id": "10.3390/rs9070684", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-09-20T16:19:12Z", "type": "Journal Article", "created": "2017-07-04", "title": "Multiple Regression Analysis for Unmixing of Surface Temperature Data in an Urban Environment", "description": "Global climate change and increasing urbanization worldwide intensify the need for a better understanding of human heat stress dynamics in urban systems. During heat waves, which are expected to increase in number and intensity, the development of urban cool islands could be a lifesaver for many elderly and vulnerable people. The use of remote sensing data offers the unique possibility to study these dynamics with spatially distributed large datasets during all seasons of the year and including day and night-time analysis. For the city of Basel 32 high-quality Landsat 8 (L8) scenes are available since 2013, enabling comprehensive statistical analysis. Therefore, land surface temperature (LST) is calculated using L8 thermal infrared (TIR) imagery (stray light corrected) applying improved emissivity and atmospheric corrections. The data are combined with a land use/land cover (LULC) map and evaluated using administrative residential units. The observed dependence of LST on LULC is analyzed using a thermal unmixing approach based on a multiple linear regression (MLR) model, which allows for quantifying the gradual influence of different LULC types on the LST precisely. Seasonal variations due to different solar irradiance and vegetation cover indicate a higher dependence of LST on the LULC during the warmer summer months and an increasing influence of the topography and albedo during the colder seasons. Furthermore, the MLR analysis allows creating predicted LST images, which can be used to fill data gaps like in SLC-off Landsat 7 ETM+ data.
", "keywords": ["multiple linear regression", "Landsat 8", "land use/land cover", "Science", "atmospheric corrections", "Q", "0211 other engineering and technologies", "land surface temperature", "02 engineering and technology", "15. Life on land", "01 natural sciences", "LST analysis", "13. Climate action", "11. Sustainability", "land surface temperature; thermal infrared data; LST analysis; atmospheric corrections; land use/land cover; multiple linear regression; urban; Landsat 8", "thermal infrared data", "urban", "0105 earth and related environmental sciences"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/9/7/684/pdf"}, {"href": "https://www.mdpi.com/2072-4292/9/7/684/pdf"}, {"href": "https://doi.org/10.3390/rs9070684"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/rs9070684", "name": "item", "description": "10.3390/rs9070684", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs9070684"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-07-04T00:00:00Z"}}, {"id": "oai:croris.hr:Publication/429606", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-09-20T16:27:49Z", "type": "Journal Article", "title": "Assessment of customer demand for public line transport in Croatia using multiple linear regression", "description": "The aim of this paper is to explore the public line transport in Croatia, determine its intensity and structure and to explore which factors have the greatest impact on the demand for public road transport. The existence and strength of dependence between the selected factors, for which are assumed to have an impact, and public road transport will be examined by multiple regression analysis. Results of the analysis should indicate which multiple regression model best meets the requirements of regression diagnostics. Furthermore, according to the selected model, the amount of demand for this type of transportation in the future will be evaluated.", "keywords": ["multiple linear regression", "public line transport ; demand quantitative forecasts methods ; multiple linear regression", "public line transport", "demand quantitative forecasts methods", "11. Sustainability"], "contacts": [{"organization": "Hess, Svjetlana, Hess, Mirano,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/oai:croris.hr:Publication/429606"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/TTEM%20-%20Technics%20technologies%20education%20management", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "oai:croris.hr:Publication/429606", "name": "item", "description": "oai:croris.hr:Publication/429606", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/oai:croris.hr:Publication/429606"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2014-01-01T00:00:00Z"}}], "links": [{"rel": "self", "type": "application/geo+json", "title": "This document as GeoJSON", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=multiple+linear+regression&f=json", "hreflang": "en-US"}, {"rel": "alternate", "type": "text/html", "title": "This document as HTML", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=multiple+linear+regression&f=html", "hreflang": "en-US"}, {"rel": "collection", "type": "application/json", "title": "Collection URL", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main", "hreflang": "en-US"}, {"type": "application/geo+json", "rel": "first", "title": "items (first)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=multiple+linear+regression&", "hreflang": "en-US"}, {"rel": "last", "type": "application/geo+json", "title": "items (last)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=multiple+linear+regression&offset=3", "hreflang": "en-US"}], "numberMatched": 3, "numberReturned": 3, "distributedFeatures": [], "timeStamp": "2026-09-20T20:01:15.644019Z"}