Special Issue "Applications of Remote Sensing for Terrestrial Ecosystem Biochemical Responses to Climate Change and Drought"

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

Deadline for manuscript submissions: 31 December 2021.

Special Issue Editors

Dr. Manuela Balzarolo
E-Mail Website
Guest Editor
University of Antwerp, Universiteitsplein 1, 2610 Wilrijk, Belgium
Interests: remote sensing; ecology; carbon cycle; drought; phenology; climate change
Dr. Frank Veroustraete
E-Mail Website
Guest Editor
University of Antwerp, Groenenborgerlaan 171, 2020 Antwerpen, Belgium
Interests: remote sensing of vegetation;ecophysiology of plants; impact assessment
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Climate change and climate extremes (e.g., drought) affect the terrestrial carbon balance and ecosystem functionalities. They modify both the rates of carbon uptake by photosynthesis (e.g., gross primary productivity) and release by total ecosystem respiration. Remote sensing has long been used to understand the impact of climate change on the spatial and seasonal variability of carbon and water balance at local and global scales.

Remotely sensed indicators can provide an effective way to obtain real-time conditions of ecosystems and offer a range of spatial and temporal observations on changes in ecosystem structure, function, and services. Remote-sensing indicators differ in their sensitivity to changes in photosynthetic status. However, no consensus has been reached regarding the most suitable indicators for quantifying and modeling the effect of climate change and its extremes on terrestrial carbon and water balance.

This Special Issue of Remote Sensing aims at the publication of both review and original research papers related to the following keyword-indicated research topics:

  • Remote sensing of climate change;
  • Remote sensing of climate extremes;
  • Remote sensing of carbon and water cycles;
  • Remote sensing of arid ecosystems;
  • Remote sensing of water limited lands;
  • Remote sensing, remote sensing of bio-geophysical variables;
  • Remote sensing of drought.

This Special Issue is open to contributions such as review papers and focus papers presenting strategies, methodologies, or approaches leading to the assimilation of remote sensing products from different platforms (e.g., in situ spectroradiometers, UAV, satellites), whether reflected in the optical range or emitted as fluorescence, far-infrared, or microwave radiation, as well as techniques based on different assimilation of remote sensing and in-situ measurements in ecological models. Data and in situ measuring methods for product validation purposes are also welcome.

Dr. Manuela Balzarolo
Dr. Frank Veroustraete
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 papers will be 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 2400 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

  • Climate change
  • Climate extremes
  • Drought
  • Carbon cycle
  • Water cycle
  • Photosynthesis
  • Light use efficiency models
  • Ecological models
  • Satellite data
  • Field spectroscopy

Published Papers (5 papers)

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Research

Open AccessArticle
Precipitation Drives the NDVI Distribution on the Tibetan Plateau While High Warming Rates May Intensify Its Ecological Droughts
Remote Sens. 2021, 13(7), 1305; https://doi.org/10.3390/rs13071305 - 29 Mar 2021
Viewed by 332
Abstract
Climate change has significantly affected the ecosystem of the Tibetan Plateau. There, temperature rises and altered precipitation patterns have led to notable changes in its vegetation growth processes and vegetation cover features. Yet current research still pays relatively little attention to the regional [...] Read more.
Climate change has significantly affected the ecosystem of the Tibetan Plateau. There, temperature rises and altered precipitation patterns have led to notable changes in its vegetation growth processes and vegetation cover features. Yet current research still pays relatively little attention to the regional climatic determinants and response patterns of such vegetation dynamics. In this study, spatial patterns in the response of the normalized difference vegetation index (NDVI) to climate change and its dynamic characteristics during the growing season were examined for the Tibetan Plateau, by using a pixel-scale-based geographically weighted regression (GWR) based on the Global Inventory Modeling and Mapping Studies (GIMMS) NDVI data, as well as data for temperature and moisture indices collected at meteorological stations, for the period 1982–2015. The results show the following. Spatial nonstationary relationships, primarily positive, were found between the NDVI and climatic factors in the Tibetan Plateau. However, warming adversely affected vegetation growth and cover in some arid and semiarid regions of the northeast and west Tibetan Plateau. Additionally, precipitation played a dominant role in the NDVI of the Tibetan Plateau in the largest area (accounting for 39.7% of total area). This suggests that increased moisture conditions considerably facilitated vegetation growth and cover in these regions during the study period. Temperature mainly played a dominant role in the NDVI in some parts of the plateau sub-cold zone and some southeastern regions of the Tibetan Plateau. In particular, the minimum temperature was the dominant driver of NDVI over a larger area than any of the other temperature indices. Furthermore, spatial regressions between NDVI dynamics and climatic variability revealed that a faster warming rate in the arid and semiarid regions impeded vegetation growth through mechanisms such as drought intensification. Moisture variability was found to act as a key factor regulating the extent of vegetation cover on the south Tibetan Plateau. Full article
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Open AccessArticle
Vegetation Productivity Dynamics in Response to Climate Change and Human Activities under Different Topography and Land Cover in Northeast China
Remote Sens. 2021, 13(5), 975; https://doi.org/10.3390/rs13050975 - 04 Mar 2021
Viewed by 380
Abstract
Net primary productivity (NPP) is the total amount of organic matter fixed by plants from the atmosphere through photosynthesis and is susceptible to the influences of climate change and human activities. In this study, we employed actual NPP (ANPP), potential NPP (PNPP), and [...] Read more.
Net primary productivity (NPP) is the total amount of organic matter fixed by plants from the atmosphere through photosynthesis and is susceptible to the influences of climate change and human activities. In this study, we employed actual NPP (ANPP), potential NPP (PNPP), and human activity-induced NPP (HNPP) based on the Hurst exponent and statistical analysis to analyze the characteristics of vegetation productivity dynamics and to evaluate the effects of climate and human factors on vegetation productivity in Northeast China (NEC). The increasing trends in ANPP, PNPP, and HNPP accounted for 81.62%, 94.90%, and 89.63% of the total area, respectively, and ANPP in 68.64% of the total area will continue to increase in the future. Climate change played a leading role in vegetation productivity dynamics, which promoted an increase in ANPP in 71.55% of the area, and precipitation was the key climate factor affecting ANPP. The aggravation of human activities, such as increased livestock numbers and intensified agricultural activities, resulted in a decrease in ANPP in the western grasslands, northern Greater Khingan Mountains, and eastern Songnen Plain. In particular, human activities led to a decrease in ANPP in 53.84% of deciduous needleleaf forests. The impact of climate change and human activities varied significantly under different topography, and the percentage of the ANPP increase due to climate change decreased from 71.13% to 53.9% from plains to urgent slopes; however, the percentage of ANPP increase due to human activities increased from 3.44% to 21.74%, and the effect of human activities on the increase of ANPP was more obvious with increasing slope. At different altitudes, the difference in the effect of these two factors was not significant. The results are significant for understanding the factors influencing the vegetation productivity dynamics in NEC and can provide a reference for governments to implement projects to improve the ecosystem. Full article
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Open AccessArticle
Asymmetry of Daytime and Nighttime Warming in Typical Climatic Zones along the Eastern Coast of China and Its Influence on Vegetation Activities
Remote Sens. 2020, 12(21), 3604; https://doi.org/10.3390/rs12213604 - 03 Nov 2020
Viewed by 502
Abstract
In this dissertation, the author adopted the normalized difference vegetation index (NDVI) and meteorological data from 1982 to 2016 of the typical climate zones in coastal areas of China to analyze the influence of daytime and nighttime warming asymmetric changes in different seasons [...] Read more.
In this dissertation, the author adopted the normalized difference vegetation index (NDVI) and meteorological data from 1982 to 2016 of the typical climate zones in coastal areas of China to analyze the influence of daytime and nighttime warming asymmetric changes in different seasons on vegetation activities during the growing season period according to the copula function theory optimized based on Markov chain Monte Carlo (MCMC). The main conclusions are as follows: (1) The seasonal daytime and nighttime warming trends of Guangdong, Jiangsu and Liaoning over the past 35 years were significant, and the daytime and nighttime warming rates were asymmetric. In spring and summer of Guangdong province, the warming rate in the daytime was higher than that at night, while, in autumn, the opposite law was observed. However, the warming rate in the daytime was lower than that at night in Jiangsu and Liaoning provinces. There were latitude differences in diurnal and nocturnal warming rate. (2) The daytime and nighttime warming influences on vegetation showed significant seasonal differences in these three regions. In Guangdong, the influence of nighttime warming on vegetation growth in spring is greater than that in summer, and the influences of daytime warming on vegetation growth from strong to weak were spring, summer and autumn. In Jiangsu, both the influences of daytime and nighttime warming on vegetation growth in summer were less than that in autumn. In Liaoning, both the influences of daytime and nighttime warming on vegetation growth from strong to weak were autumn, spring and summer. (3) In Guangdong, Jiangsu and Liaoning provinces, their maximum temperature (Tmax) and minimum temperature (Tmin) and the joint probability distribution functions of NDVI, all had little effect on NDVI when Tmax and Tmin respectively reached their minimum values, but their influences on NDVI were obvious when Tmax and Tmin respectively reached their maximum values. (4) The smaller the return period, the larger the range of climate factor and NDVI, which has indicated that when the climate factor is certain, the NDVI is more likely to have a smaller return period, and the frequency of NDVI over a certain period is higher. In addition, the larger the climate factor, the greater the return period is and NDVI is less frequent over a certain period of time. This research can help with deep understanding of the dynamic influence of seasonal daytime and nighttime asymmetric warming on the vegetation in typical coastal temperature zones of China under the background of global climate change. Full article
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Open AccessArticle
Spectral Response Assessment of Moss-Dominated Biological Soil Crust Coverage Under Dry and Wet Conditions
Remote Sens. 2020, 12(7), 1158; https://doi.org/10.3390/rs12071158 - 04 Apr 2020
Cited by 1 | Viewed by 802
Abstract
Biological soil crusts (BSCs) are a major functional vegetation unit, covering extensive parts of drylands worldwide. Therefore, several multispectral indices have been proposed to map the spatial distribution and coverage of BSCs. BSCs are composed of poikilohydric organisms, the activity of which is [...] Read more.
Biological soil crusts (BSCs) are a major functional vegetation unit, covering extensive parts of drylands worldwide. Therefore, several multispectral indices have been proposed to map the spatial distribution and coverage of BSCs. BSCs are composed of poikilohydric organisms, the activity of which is sensitive to water availability. However, studies on dry and wet BSCs have seldom considered the mixed coverage gradient that is representative of actual field conditions. In this study, in situ spectral data and photographs of 136 pairs of dry and wet plots were collected to determine the influence of moisture conditions on BSC coverage detection. Then, BSC spectral reflectance and continuum removal (CR) reflectance responses to wetting were analyzed. Finally, the responses of four commonly used indices (i.e., normalized difference vegetation index (NDVI); crust index (CI); biological soil crust index (BSCI); and band depth of absorption feature after CR in the red band, (BD_red)), calculated from in situ hyperspectral data resampled to two multispectral data channels (Landsat-8 and Sentinel-2), were compared in dry and wet conditions. The results indicate that: (i) on average, the estimated BSC coverage using red-green-blue (RGB) images is 14.98% higher in wet than in dry conditions (P < 0.001); (ii) CR reflectance features of wet BSCs are more obvious than those of dry BSCs in both red and red-edge bands; and (iii) NDVI, CI, and BSCI for BSC coverage of 0%–60% under dry and wet conditions are close to those of dry and wet bare sand, respectively. NDVI and BD_red cannot separate dead wood and BSC with low coverage. This study demonstrates that low-coverage moss-dominated BSC is not easily detected by the four indices. In the future, remote-sensing data obtained during the rainy season with red and red-edge bands should be considered to detect BSCs. Full article
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Open AccessFeature PaperArticle
Seasonal Adaptation of the Thermal-Based Two-Source Energy Balance Model for Estimating Evapotranspiration in a Semiarid Tree-Grass Ecosystem
Remote Sens. 2020, 12(6), 904; https://doi.org/10.3390/rs12060904 - 11 Mar 2020
Cited by 5 | Viewed by 1393
Abstract
The thermal-based two-source energy balance (TSEB) model has accurately simulated energy fluxes in a wide range of landscapes with both remote and proximal sensing data. However, tree-grass ecosystems (TGE) have notably complex heterogeneous vegetation mixtures and dynamic phenological characteristics presenting clear challenges to [...] Read more.
The thermal-based two-source energy balance (TSEB) model has accurately simulated energy fluxes in a wide range of landscapes with both remote and proximal sensing data. However, tree-grass ecosystems (TGE) have notably complex heterogeneous vegetation mixtures and dynamic phenological characteristics presenting clear challenges to earth observation and modeling methods. Particularly, the TSEB modeling structure assumes a single vegetation source, making it difficult to represent the multiple vegetation layers present in TGEs (i.e., trees and grasses) which have different phenological and structural characteristics. This study evaluates the implementation of TSEB in a TGE located in central Spain and proposes a new strategy to consider the spatial and temporal complexities observed. This was based on sensitivity analyses (SA) conducted on both primary remote sensing inputs (local SA) and model parameters (global SA). The model was subsequently modified considering phenological dynamics in semi-arid TGEs and assuming a dominant vegetation structure and cover (i.e., either grassland or broadleaved trees) for different seasons (TSEB-2S). The adaptation was compared against the default model and evaluated against eddy covariance (EC) flux measurements and lysimeters over the experimental site. TSEB-2S vastly improved over the default TSEB performance decreasing the mean bias and root-mean-square-deviation (RMSD) of latent heat (LE) from 40 and 82 W m−2 to −4 and 59 W m−2, respectively during 2015. TSEB-2S was further validated for two other EC towers and for different years (2015, 2016 and 2017) obtaining similar error statistics with RMSD of LE ranging between 57 and 63 W m−2. The results presented here demonstrate a relatively simple strategy to improve water and energy flux monitoring over a complex and vulnerable landscape, which are often poorly represented through remote sensing models. Full article
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Tentative title: Precipitation significantly impacts the NDVI distribution in the Tibetan Plateau, while high warming rate may enhance ecological drought

Kewei Jiao, Jiangbo Gao*, Zhihua Liu, Shaohong Wu

Abstract: To understand the complex relationship between climate change and vegetation activity in the Tibetan Plateau, the spatial distribution and dynamic characteristics of the response of NDVI to climate change from 1982 to 2015 were investigated by the geographically weighted regression (GWR) model. The GWR was run based on the combined datasets of satellite vegetation index (GIMMS NDVI) and climate observation (temperature and moisture) from meteorological stations nationwide. The results noted that the spatial non-stationary relationship between NDVI and surface temperature has appeared in the Tibetan Plateau. The significant negative temperature-vegetation relationship was distributed in northeastern and western parts of the Tibetan Plateau. And then, by comparing the normalized regression coefficients for different climate factors, regions with precipitation dominants for NDVI were distributed most widely (39.7%), and regions with temperature dominants for NDVI were distributed in the plateau sub-cold zone and Southeast Tibetan Plateau, where the annual mean minimum temperature accounts for the largest areas. In addition, regression coefficients between NDVI dynamics and climate variability indicated that the higher warming rate could result in the weakened vegetation activity through some mechanisms such as enhanced drought (Arid/Semi-arid region), while the moisture variability could mediate the hydrothermal conditions for the variation of vegetation activity (Southern part).

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