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Special Issue "Remote Sensing of Ecosystems in Cold Regions"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Ecological Remote Sensing".

Deadline for manuscript submissions: closed (31 May 2022) | Viewed by 4153

Special Issue Editors

Dr. Youngwook Kim
E-Mail Website
Guest Editor
Department of Biology, United Arab Emirates University, Al Ain 15551, UAE
Interests: remote sensing of freeze/thaw status and vegetation dynamics; climate and environmental change impact on terrestrial ecosystems
Dr. Ranjeet John
E-Mail Website
Guest Editor
Department of Biology, Joint appointment-Department of Sustainability Landscape Ecology & Ecosystem Science (LEES), South Dakota State University, Brookings, SD 57007, USA
Interests: grassland degradation; semiarid ecosystems; agro-ecosystems; GPP; ET; disturbance ecology; species richness; climate change; drought; UAV; remote sensing; land cover/land use change; climate–land interaction
Dr. Jennifer D. Watts
E-Mail Website
Guest Editor
Woodwell Climate Research Center, Falmouth, MA 02540, USA
Interests: global wetlands; arctic-boreal regions; remote sensing papers: Statistical upscaling of ecosystem CO2 fluxes across the terrestrial tundra and boreal domain: Regional patterns and uncertainties
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Cold regions (including high-latitude and high-elevation landscapes) and areas of permafrost and glacial ice cover, are experiencing ecosystem changes caused by global warming. Remote sensing has become increasingly important for monitoring and understanding the patterns and mechanisms of change in cold region ecosystems where the frozen season is a significant constraint on eco-hydrological processes and functionings. Recent advances in remote sensing include the development of new sensors (multispectral, hyperspectral, thermal, microwave, SAR, and SIF), airborne platforms (UAVs), and big data analytics. These technologies provide many opportunities to quantify hydrological, ecological, and cryospheric variables with characterizing cold region ecosystems. The aim of this Special Issue is to collect state-of-the-art research in remote sensing technology and applications of cold region ecosystems. Studies using multi-scale and multi-component data (in-situ measurements, satellite observations, and modeling) are also welcome. Submissions on the following topics are invited (but not limited to):

  • Remote sensing of cold region ecosystems, including ecosystem productivity, phenology, greening/browning, evapotranspiration, and arctic species distribution;
  • Tundra vegetation ecology, boreal ecosystem ecology, boreal wetlands;
  • Land cover/land use change;
  • Disturbance and recovery of cold region ecosystems;
  • Landscape freeze/thaw status;
  • Remote sensing of snow/ice, permafrost;
  • Remote sensing of water bodies;
  • Glacial mass, movement, and melting;
  • Multiple scale monitoring (field/plot, landscape, regional) and bottom-up scaling;
  • Application of new algorithm, comparison/evaluation of methods.

Dr. Youngwook Kim
Dr. Ranjeet John
Dr. Jennifer D. Watts
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • cold regions
  • remote sensing
  • microwave
  • SAR
  • UAV
  • tundra
  • boreal forests
  • permafrost

Published Papers (4 papers)

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Research

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Article
Remotely Sensed Winter Habitat Indices Improve the Explanation of Broad-Scale Patterns of Mammal and Bird Species Richness in China
Remote Sens. 2022, 14(3), 794; https://doi.org/10.3390/rs14030794 - 08 Feb 2022
Cited by 2 | Viewed by 618
Abstract
Climate change is transforming winter environmental conditions rapidly. Shifts in snow regimes and freeze/thaw cycles that are unique to the harsh winter season can strongly influence ecological processes and biodiversity patterns of mammals and birds. However, the role of the winter environment in [...] Read more.
Climate change is transforming winter environmental conditions rapidly. Shifts in snow regimes and freeze/thaw cycles that are unique to the harsh winter season can strongly influence ecological processes and biodiversity patterns of mammals and birds. However, the role of the winter environment in structuring a species richness pattern is generally downplayed, especially in temperate regions. Here we developed a suite of winter habitat indices at 500 m spatial resolution by fusing MODIS snow products and NASA MEaSUREs daily freeze/thaw records from passive microwave sensors and tested how these indices could improve the explanation of species richness patterns across China. We found that the winter habitat indices provided unique and mutually complementary environmental information compared to the commonly used Dynamic Habitat Indices (DHIs). Winter habitat indices significantly increased the explanatory power for species richness of all mammal and bird groups. Particularly, winter habitat indices contributed more to the explanation of bird species than mammals. Regarding the independent contribution, winter season length made the largest contributions to the explained variance of winter birds (30%), resident birds (27%), and mammals (18%), while the frequency of snow-free frozen ground contributed the most to the explanation of species richness for summer birds (23%). Our research provides new insights into the interpretation of broad-scale species diversity, which has great implications for biodiversity assessment and conservation. Full article
(This article belongs to the Special Issue Remote Sensing of Ecosystems in Cold Regions)
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Article
Greening of the Qinghai–Tibet Plateau and Its Response to Climate Variations along Elevation Gradients
Remote Sens. 2021, 13(18), 3712; https://doi.org/10.3390/rs13183712 - 17 Sep 2021
Cited by 5 | Viewed by 1044
Abstract
The vegetation of the Qinghai–Tibet Plateau (QTP) is vital to the global climate change and ecological security of China. However, the impact of climate variation on the spatial pattern and zonal distribution of vegetation in the QTP remains unclear. Accordingly, we used multisource [...] Read more.
The vegetation of the Qinghai–Tibet Plateau (QTP) is vital to the global climate change and ecological security of China. However, the impact of climate variation on the spatial pattern and zonal distribution of vegetation in the QTP remains unclear. Accordingly, we used multisource remote-sensing vegetation indices (GIMMS-LAI, GIMMS NDVI, GLOBMAP LAI, MODIS EVI, MODIS NDVI, and MODIS NIRv), climate data, a digital elevation model, and the moving window method to investigate the changes in vegetation greenness and its response to climate variations in the QTP from 2001 to 2016. Results showed that the vegetation was greening in the QTP, which might be attributed to the increases in temperature and radiation. By contrast, the browning of vegetation may be caused by drought. Notably, the spatial patterns of vegetation greenness and its variations were linearly correlated with climate at low altitudes, and vegetation greenness was non-linearly correlated with climate at high altitudes. The Northwestern QTP needs to be focused on in regard to positive and decreased VGEG (vegetation greenness along the elevation gradient). The significantly positive VGEG was up to (34.37 ± 2.21) % of the QTP, which indicated a homogenization of vegetation greenness on elevation. This study will help us to understand the spatial distribution of vegetation greenness and VGEG in the QTP under global warming, and it will benefit ecological environment management, policymaking, and future climate and carbon sink (source) prediction. Full article
(This article belongs to the Special Issue Remote Sensing of Ecosystems in Cold Regions)
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Article
The Impact of Climate Change on the Surface Albedo over the Qinghai-Tibet Plateau
Remote Sens. 2021, 13(12), 2336; https://doi.org/10.3390/rs13122336 - 15 Jun 2021
Cited by 3 | Viewed by 1009
Abstract
Albedo is a characterization of the Earth’s surface ability to reflect solar radiation, and control the amount of solar radiation absorbed by the land surface. Within the context of global warming, the temporal and spatial changes of the albedo and its response to [...] Read more.
Albedo is a characterization of the Earth’s surface ability to reflect solar radiation, and control the amount of solar radiation absorbed by the land surface. Within the context of global warming, the temporal and spatial changes of the albedo and its response to climate factors remain unclear. Based on MCD43A3 (V005) albedo and meteorological data (i.e., temperature and precipitation), we analyzed the spatiotemporal variations of albedo (2000–2016) and its responses to climate change during the growing season on the Qinghai-Tibet Plateau (QTP). The results indicated an overall downward trend in the annual albedo during the growing season, the decrease rate was 0.25%/decade, and the monthly albedo showed a similar trend, especially in May, when the decrease rate was 0.53%/decade. The changes also showed regional variations, such as for the annual albedo, the areas with significant decrease and increase in albedo were 181.52 × 103 km2 (13.10%) and 48.82 × 103 km2 (3.52%), respectively, and the intensity of albedo changes in low-elevation areas was more pronounced than in high-elevation areas. In addition, the annual albedo-temperature/precipitation relationships clearly differed at different elevations. The albedo below 2000 m and at 5000–6000 m was mainly negatively correlated with temperature, while at 2000–4000 m it was mainly negatively correlated with precipitation. The contemporaneous temperature could negatively impact the monthly albedo in significant ways at the beginning of the growing season (May and June), whereas in the middle of the growing season (July and August), the albedo was mainly negatively correlated with precipitation, and at the end of the growing season (September), the albedo showed a weak correlation with temperature/precipitation. Full article
(This article belongs to the Special Issue Remote Sensing of Ecosystems in Cold Regions)
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Technical Note
Increasing Atmospheric Aridity Moderates the Accelerated Rate of Vegetation Green-Up Induced by Rising CO2 and Warming
Remote Sens. 2022, 14(16), 3946; https://doi.org/10.3390/rs14163946 - 14 Aug 2022
Viewed by 312
Abstract
The rate of vegetation green-up (RVG) indicates the ability of vegetation to respond to changes in climatic conditions. Understanding long-term RVG trends can clarify the changes in how quickly the vegetation grows from dormancy to maturity with time. However, how RVG trends respond [...] Read more.
The rate of vegetation green-up (RVG) indicates the ability of vegetation to respond to changes in climatic conditions. Understanding long-term RVG trends can clarify the changes in how quickly the vegetation grows from dormancy to maturity with time. However, how RVG trends respond to environmental variables and variable interactions remains unknown. We examined the long-term RVG trends (1981–2018) over the northern extratropics and determined the influence of environment variables and interactions between variables on the RVG trends based on the Global Land Surface Satellite leaf area index and a multivariable regression considering interactions between variables (MRCI). Our results showed a persistent increase in RVG at 0.020% (8-day)−1 year−1 over the entire region. Except for shrublands (−0.032% (8-day)−1 year−1), RVG trends increased significantly, particularly in woody savannas (0.095% (8-day)−1 year−1) and mixed forests (0.076% (8-day)−1 year−1). The relative importance of interactive effects (RIIAE) to the RVG trends is roughly 30%. Rising CO2, enhanced vapor pressure deficit (VPD), and warming are the primary factors affecting the RVG trends, both at the pixel and the biome scales. The accelerated RVG is triggered by both rising CO2 and warming but is partially offset by increased VPD. Our findings shed light on the relative contribution of variable interactions and assessed the relationship between environmental factors and RVG trends across different biomes, hence strengthening our knowledge of vegetation spring green-up in response to global change. Full article
(This article belongs to the Special Issue Remote Sensing of Ecosystems in Cold Regions)
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