Assessing Spatiotemporal Variations of Landsat Land Surface Temperature and Multispectral Indices in the Arctic Mackenzie Delta Region between 1985 and 2018

Leveraging synergies between UAV and Landsat 8 sensors to evaluate the impact of pale lichen biomass on land surface temperature in heath tundra ecosystems

Pale terricolous lichens are a vital component of Arctic ecosystems, significantly contributing to carbon balance, energy regulation, and serving as a primary food source for reindeer. Their characteristically high albedo also impacts land surface temperature (LST) dynamics across various spatial scales. However, remote sensing of lichens is challenging due to their complex spectral signatures and large spatial variations in coverage and biomass even within local landscape scales. This study evaluates the influence of pale lichens on LST at local and landscape scales by integrating RGB, multispectral, and thermal infrared imagery from an Unmanned Aerial Vehicle (UAV) with multi-temporal Landsat 8 thermal data. An Extreme Gradient Boosting algorithm was employed to map pale lichen biomass, areal extent, and the occurrence of major plant functional types in the sub-arctic heath tundra landscape in the Jávrrešduottar and Sieiddečearru areas on the Finland-Norway border. Generalized Additive Models (GAMs) were used to elucidate the factors affecting LST. The UAV model accurately predicted pale lichen biomass (R2 0.63) and vascular vegetation cover (R2 0.70). GAMs revealed that pale lichens significantly influence thermal regimes, with increased biomass leading to decreased LST, an effect more pronounced at the landscape scale (deviance explained 47.26 % and 65.8 % for local and landscape models, respectively). Pale lichen biomass was identified as the second most important variable affecting LST at both scales, with elevation being the most important variable. This research demonstrates the capability of UAV-derived models to capture the heterogeneous and fine-scale structure of tundra ecosystems. Furthermore, it underscores the effectiveness of combining high spatial resolution UAV and high temporal resolution satellite platforms. Finally, this study highlights the pivotal role of pale lichens in Arctic thermal dynamics and showcases how advanced remote sensing techniques can be used for ecological monitoring and management.

Influence of landscape features on urban land surface temperature: Scale and neighborhood effects

Higher land surface temperature (LST) in cities than its surrounding areas presents a major sustainability challenge for cities. Adaptation and mitigation of the increased LST require in-depth understanding of the impacts of landscape features on LST. We studied the influences of different landscape features on LST in five large cities across China to investigate how the features of a specific urban landscape (endogenous features), and neighboring environments (exogenous features) impact its LST across a continuum of spatial scales. Surprisingly, results show that the influence of endogenous landscape features (E_endo) on LST can be described consistently across all cities as a nonlinear function of grain size (g_s) and neighbor size (n_s) (E_endo = βn_sg_s^-0.5, where β is a city-specific constant) while the influence of exogenous features (E_exo) depends only on neighbor size (n_s) (E_exo = γ-εn_s^0.5, where γ and ε are city-specific constants). In addition, a simple relationship describing the relative strength of endogenous and exogenous impacts of landscape features on LST was found (E_endo > E_exo if n_s > kg_s^2/5, where k is a city-specific parameter; otherwise, E_endo < E_exo). Overall, vegetation alleviates 40%-60% of the warming effect of built-up while surface wetness intensifies or reduces it depending on climate conditions. This study reveals a set of unifying quantitative relationships that effectively describes landscape impacts on LST across cities, grain and neighbor sizes, which can be instrumental towards the design of sustainable cities to deal with increasing temperature.

Trends in Satellite Earth Observation for Permafrost Related Analyses—A Review

Climate change and associated Arctic amplification cause a degradation of permafrost which in turn has major implications for the environment. The potential turnover of frozen ground from a carbon sink to a carbon source, eroding coastlines, landslides, amplified surface deformation and endangerment of human infrastructure are some of the consequences connected with thawing permafrost. Satellite remote sensing is hereby a powerful tool to identify and monitor these features and processes on a spatially explicit, cheap, operational, long-term basis and up to circum-Arctic scale. By filtering after a selection of relevant keywords, a total of 325 articles from 30 international journals published during the last two decades were analyzed based on study location, spatio-temporal resolution of applied remote sensing data, platform, sensor combination and studied environmental focus for a comprehensive overview of past achievements, current efforts, together with future challenges and opportunities. The temporal development of publication frequency, utilized platforms/sensors and the addressed environmental topic is thereby highlighted. The total number of publications more than doubled since 2015. Distinct geographical study hot spots were revealed, while at the same time large portions of the continuous permafrost zone are still only sparsely covered by satellite remote sensing investigations. Moreover, studies related to Arctic greenhouse gas emissions in the context of permafrost degradation appear heavily underrepresented. New tools (e.g., Google Earth Engine (GEE)), methodologies (e.g., deep learning or data fusion etc.) and satellite data (e.g., the Methane Remote Sensing LiDAR Mission (Merlin) and the Sentinel-fleet) will thereby enable future studies to further investigate the distribution of permafrost, its thermal state and its implications on the environment such as thermokarst features and greenhouse gas emission rates on increasingly larger spatial and temporal scales.

Surface Roughness Estimation in the Orog Nuur Basin (Southern Mongolia) Using Sentinel-1 SAR Time Series and Ground-Based Photogrammetry

This study demonstrates an application-oriented approach to estimate area-wide surface roughness from Sentinel-1 time series in the semi-arid environment of the Orog Nuur Basin (southern Mongolia) to support recent geomorphological mapping efforts. The relation of selected mono- and multi-temporal SAR features and roughness is investigated by using an empirical multi-model approach and selected 1D and 2D surface roughness indices. These indices were obtained from 48 high-resolution ground-based photogrammetric digital elevation models, which were acquired during a single field campaign. The analysis is backed by a time series analysis, comparing Sentinel-1 features to temporal-corresponding observations and reanalysis datasets on soil moisture conditions, land surface temperature, occurrence of precipitation events, and presence and development of vegetation. Results show that Sentinel-1 features are hardly sensitive to the changing surface conditions over none to sparsely vegetated land, indicating very dry conditions throughout the year. Consequently, surface roughness is the dominating factor altering SAR intensity. The best correlation is found for the combined surface roughness index Z-Value (ratio between the root mean square height and the correlation length) and the mean summer VH intensity with an r2 coefficient of 0.83 and an Root-Mean-Square Error of 0.032.

35 Years of Vegetation and Lake Dynamics in the Pechora Catchment, Russian European Arctic

High-latitude regions are a hot spot of global warming, but the scarce availability of observations often limits the investigation of climate change impacts over these regions. However, the utilization of satellite-based remote sensing data offers new possibilities for such investigations. In the present study, vegetation greening, vegetation moisture and lake distribution derived from medium-resolution satellite imagery were analyzed over the Pechora catchment for the last 35 years. Here, we considered the entire Pechora catchment and the Pechora Delta region, located in the northern part of European Russia, and we investigated the vegetation and lake dynamics over different permafrost zones and across the two major biomes, taiga, and tundra. We also evaluated climate data records from meteorological stations and re-analysis data to find relations between these dynamics and climatic behavior. Considering the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Moisture Index (NDMI) in the summer, we found a general greening and moistening of the vegetation. While vegetation greenness follows the evolution of summer air temperature with a delay of one year, the vegetation moisture dynamics seems to better concur with annual total precipitation rather than summer precipitation, and also with annual snow water equivalent without lag. Both NDVI and NDMI show a much higher variability across discontinuous permafrost terrain compared to other types. Moreover, the analyses yielded an overall decrease in the area of permanent lakes and a noticeable increase in the area of seasonal lakes. While the first might be related to permafrost thawing, the latter seems to be connected to an increase of annual snow water equivalent. The general consistency between the indices of vegetation greenness and moisture based on satellite imagery and the climate data highlights the efficacy and reliability of combining Landsat satellite data, ERA-Interim reanalysis and meteorological data to monitor temporal dynamics of the land surface in Arctic areas.