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Special Issue "Advances in Remote Sensing for Monitoring and Characterising Vegetation Responses to Changing and Extreme Climatic Conditions"

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

Deadline for manuscript submissions: 30 November 2023 | Viewed by 3582

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Special Issue Editors

Dr. Elhadi Adam

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School of Geography, Archaeology and Environmental Studies, University of the Witwatersrand, Private Bag 3, Wits 2050, Johannesburg, South Africa
Interests: hyperspectral and multispectral remote sensing; GIS modelling for environmental and agriculture applications
Special Issues, Collections and Topics in MDPI journals

Dr. Anna Jarocińska

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Guest Editor

Department of Geoinformatics, Cartography and Remote Sensing, Chair of Geomatics and Information Systems, Faculty of Geography and Regional Studies, University of Warsaw, Poland, ul. Krakowskie Przedmieście 30, 00-927 Warsaw, Poland
Interests: hyperspectral imaging; vegetation classification; biophysical remote sensing; vegetation index; vegetation condition
Special Issues, Collections and Topics in MDPI journals

Dr. Solomon Newete

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Guest Editor

Agricultural Research Council-Natural Resources and Engineering (ARC-NRE), Arcadia, Pretoria 0001, Gauteng, South Africa
Interests: remote sensing；Phytoremediation；Biocontrol；Invasive alien species；Food security；Agriculture research；Heavy metal accumulation；Climate change and agriculture

Dr. Mustafa Ustuner

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Guest Editor

Department of Geomatic Engineering, Artvin Coruh University, Artvin, Turkey
Interests: remote sensing; SAR remote sensing; geomatic engineering; polarimetric SAR

Dr. Siti Aekbal

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Guest Editor

Head of Sustainable Technology, Institute for Biodiversity and Sustainable Development, Universiti Teknologi MARA, Shah Alam, Malaysia
Interests: GIS, remote sensing, urban climate, climate change, change assessment impacts

Special Issue Information

Dear Colleagues,

Vegetation cover plays an essential ecological role in energy exchange and the material cycle. For example, in the terrestrial ecosystem, vegetation has substantial effects on the interception of rainfall, the runoff yield mechanism, soil and water conservation, and desertification prevention. Therefore, vegetation dynamics can be used to examine the terrestrial environmental condition, the structures and functions of landscape systems, and ecological processes.

Unfortunately, climate extremes and climate change increase stressors that weaken plant resilience, disrupting forest structure and ecosystem services. The negative consequences of natural hazards associated with climate extremes, such as rising temperatures, lead to more frequent droughts, wildfires, invasive pest outbreaks, and the loss and degradation of vegetation species and communities in various ecosystems and biodiversity.

Thus, understanding the interdependence between extreme climate threats and vegetation dynamics and their response is needed to examine the mechanism of the climate–vegetation system and develop an effective management programme towards ecological conservation and targeted restoration policies for land use planning and environmental management.

This Special Issue (SI) aims to bring together multidisciplinary scientists and specialists to develop Earth observation approaches that can improve our understanding of the impacts of climate extremes and change on vegetation ecosystems and therefore contribute to ecological sustainability management. The SI topics include, but are not exclusive to, the following:

Vegetation cover changes (short-term and decadal degradation);
Vegetation phenological response to climate change;
Vegetation stress and drought;
Net primary production;
Biodiversity and extreme climate conditions;
Vegetation damage and restoration after extreme climate conditions;
Vegetation and wildfire;
Pest outbreak due to climate change;
Vegetation dynamic and time series techniques for extracting climate change indicators;
Vegetation biochemical and biophysical parameters and climate conditions.

Dr. Elhadi Adam
Dr. Anna Jarocińska
Dr. Solomon Newete
Dr. Mustafa Ustuner
Dr. Siti Aekbal
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 2700 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

vegetation dynamic
climate change
vegetation recovery
extreme climatic conditions

Published Papers (4 papers)

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Research

Open AccessArticle

Vegetation Greenness Sensitivity to Precipitation and Its Oceanic and Terrestrial Component in Selected Biomes and Ecoregions of the World

and

Remote Sens. 2023, 15(19), 4706; https://doi.org/10.3390/rs15194706 - 26 Sep 2023

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Abstract

In this study, we conducted a global assessment of the sensitivity of vegetation greenness (VGS) to precipitation and to the estimated Lagrangian precipitation time series of oceanic (PLO) and terrestrial (PLT) origin. The study was carried out for terrestrial ecosystems consisting of 9 biomes and 139 ecoregions during the period of 2001–2018. This analysis aimed to diagnose the vegetative response of vegetation to the dominant component of precipitation, which is of particular interest considering the hydroclimatic characteristics of each ecoregion, climate variability, and changes in the origin of precipitation that may occur in the context of climate change. The enhanced vegetation index (EVI) was used as an indicator of vegetation greenness. Without consideration of semi-arid and arid regions and removing the role of temperature and radiation, the results show the maximum VGS to precipitation in boreal high-latitude ecoregions that belong to boreal forest/taiga: temperate grasslands, savannas, and shrublands. Few ecoregions, mainly in the Amazon basin, show a negative sensitivity. We also found that vegetation greenness is generally more sensitive to the component that contributes the least to precipitation and is less stable throughout the year. Therefore, most vegetation greenness in Europe is sensitive to changes in PLT and less to PLO. In contrast, the boreal forest/taiga in northeast Asia and North America is more sensitive to changes in PLO. Finally, in most South American and African ecoregions, where PLT is crucial, the vegetation is more sensitive to PLO, whereas the contrast occurs in the northern and eastern ecoregions of Australia. Full article

(This article belongs to the Special Issue Advances in Remote Sensing for Monitoring and Characterising Vegetation Responses to Changing and Extreme Climatic Conditions)

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Open AccessArticle

Changes in Vegetation Resistance and Resilience under Different Drought Disturbances Based on NDVI and SPEI Time Series Data in Jilin Province, China

and

Remote Sens. 2023, 15(13), 3280; https://doi.org/10.3390/rs15133280 - 26 Jun 2023

Viewed by 610

Abstract

Extreme drought is increasing in frequency and intensity in many regions globally. Understanding the changes in vegetation resistance and resilience under aggravated drought is essential for maintaining regional ecosystem stability. In this study, a drought event–vegetation response framework was developed to explore vegetation resistance and resilience changes. The normalized difference vegetation index (NDVI) was correlated with the standardized precipitation evapotranspiration index (SPEI) at multiple timescales to screen out the vegetation response time to drought. Then, the SPEI for the response time was detected using run theory to identify drought events during the period 2000–2017. Finally, drought-induced NDVI anomaly changes were identified using a sliding window to explore the changes in resistance and resilience to drought. This study focuses on Jilin province, China, which contains a famous environmentally vulnerable area. The results illustrate that the response time of vegetation to drought is 3 months. The northwest of Jilin province is considered to be drought-vulnerable because it has suffered the most drought events, i.e., 19–21 times, with severities in the range of 2.6–3.2 and durations in the range of 3.6–4.1 months. Grassland shows the weakest resistance and the strongest resilience, and tree cover shows the strongest resistance and the weakest resilience under severe drought disturbance among all vegetation. As the severity and duration of drought increase, the resistance decreases, and the resilience increases. During the growing season, the drought from May to July significantly impacts the vegetation resistance. Drought occurring from June to July has much less impact on resilience. Drought in August to September has less impact on resistance and a more significant impact on resilience. The results of this study may increase our knowledge regarding the response of vegetation to drought and guide ecosystem stability restoration. Full article

(This article belongs to the Special Issue Advances in Remote Sensing for Monitoring and Characterising Vegetation Responses to Changing and Extreme Climatic Conditions)

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Open AccessArticle

Net Primary Productivity Estimation of Terrestrial Ecosystems in China with Regard to Saturation Effects and Its Spatiotemporal Evolutionary Impact Factors

Shuaiyang Qi

Huaiqing Zhang

and

Meng Zhang

Remote Sens. 2023, 15(11), 2871; https://doi.org/10.3390/rs15112871 - 31 May 2023

Viewed by 754

Abstract

The net primary productivity (NPP) of vegetation holds a pivotal character for the global carbon balance as a key parameter for characterizing terrestrial ecological processes. The most commonly used indices for estimating vegetation NPP, for instance, the normalized difference vegetation index (NDVI), often suffer from saturation issues that can compromise the accuracy of NPP estimation. This research utilizes a new vegetation index based on the radial basis function (RBF) to estimate vegetation NPP in Chinese terrestrial ecosystems over the past two decades (2001–2020) and investigates the spatiotemporal variation characteristics of NPP and the driving mechanisms. The results indicate that the kernel vegetation index (kNDVI) can effectively alleviate the saturation problem and significantly improve the accuracy of NPP estimation compared to NDVI. Over the past two decades, the NPP of Chinese terrestrial vegetation ranged from 64.13 to 79.72 g C/m², with a mean value of 72.75 g C/m², showing a fluctuating upward trend. Changes in the NPP of terrestrial ecosystems in China are mainly affected by precipitation. The dominant factors influencing NPP changes varied over time and had different impacts. For instance, in the period of 2001–2005 the climate had a positive effect on NPP changes, with the dominant factors being evaporation and precipitation. However, in the period of 2010–2015 the dominant climate factors shifted to evaporation and temperature, and their effect on NPP changes became negative. The outcomes of this research aim to serve as a foundation for carbon cycle research and ecosystem environment construction in China. Full article

(This article belongs to the Special Issue Advances in Remote Sensing for Monitoring and Characterising Vegetation Responses to Changing and Extreme Climatic Conditions)

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Open AccessArticle

Monitoring the Impact of Heat Damage on Summer Maize on the Huanghuaihai Plain, China

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Remote Sens. 2023, 15(11), 2773; https://doi.org/10.3390/rs15112773 - 26 May 2023

Viewed by 506

Abstract

As an important food crop, summer maize is widely planted all over the world. Monitoring its growth and output is of great significance for world food security. With the trend of global warming and deterioration, the frequency of high temperature and heat damage affecting summer corn has been increasing in the past ten years. Therefore, there is an increasing demand for monitoring the high temperature and heat damage of summer maize. At present, there are nearly a hundred indices or methods for research on high temperature and heat damage. However, research based on the vegetation index cannot fully describe the damage caused by high-temperature thermal damage, and there is an obvious asynchrony effect. Research based on hyperspectral remote sensing has many inconveniences in data acquisition and complex physical model construction. Therefore, this study uses remote sensing data, including MODIS surface reflection data, MODIS land surface temperature products, as well as ground observation data and statistical data, combined with multiple remote sensing indices and land surface temperature, to construct a remote sensing index, LSHDI (land surface heat damage index). The LSHDI first searches for a location with the worst vegetation growth conditions in the three-dimensional feature space based on the LST (land surface temperature), the normalized difference vegetation index (NDVI), and the land surface water index (LSWI). Then, it calculates the distance between each point and this location to measure the degree of vegetation affected by high temperature and heat damage. Finally, because there is no reliable disaster verification dataset that has been published at present, this study uses soil moisture as a reference to explain the performance and stability of the LSHDI. The results showed that their coefficient of determination was above 0.5 and reached a significance level of 0.01. The LSHDI can well-reflect the high temperature and heat damage of land surface vegetation and can provide important data support and references for agricultural management departments. Full article

(This article belongs to the Special Issue Advances in Remote Sensing for Monitoring and Characterising Vegetation Responses to Changing and Extreme Climatic Conditions)

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