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The Impact of Extreme Climatic and Disturbance Events on Vegetation Using Remote Sensing

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: closed (30 November 2021) | Viewed by 31648

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Prof. Dr. Lei Fan

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Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geographical Sciences, Southwest University, Chongqing 400715, China
Interests: passive microwave remote sensing; vegetation optical depth; carbon balance
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Prof. Dr. Xiuzhi Chen

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School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou 510275, China
Interests: remote sensing of tropical forest

Dr. Frédéric Frappart

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Interactions Sol Plante Atmosphère, UMR 1391 INRAE/Bordeaux Science Agro 71, Avenue Edouard Bourlaux, 33882 Villenave d'Ornon, France
Interests: remote sensing; water cycle; carbon cycle; wetlands
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Prof. Dr. Yongxian Su

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Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangzhou 510070, China
Interests: low carbon city; monitoring of CO₂ emissions based on multi-source remote sensing

Dr. Yuanwei Qin

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Department of Microbiology and Plant Biology, University of Oklahoma, Norman, OK 73019, USA
Interests: forest resources and ecosystem; forest carbon; agriculture; environmental remote sensing
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Special Issue Information

Dear Colleagues,

Extreme climatic events (e.g., heat waves, drought, and flood) and disturbance events (e.g., fire and insect outbreak) are predicted to increase in frequency and magnitude as a consequence of global warming, but their ecological effects are poorly understood—particularly in forest ecosystems. Remote sensing data’s accessibility, diversity, quality, and computing capacity provide new opportunities to understand the impact of extreme climatic and disturbance events on vegetation. Long-term and synchronous remote sensing observations have allowed for an improved understanding of ecosystems dynamics globally affected by extreme climatic and disturbance events in the last several decades. This is particularly important for understanding the recovery of vegetation in the post-disturbance period, which is the key to understand resilience of vegetation under the severe environmental stress from climate change and disturbances. Low recovery or resilience could threaten the ecosystem functions and services such as carbon cycling and biodiversity. The use of innovative techniques (e.g., machine learning, artificial intelligence) and new remote sensing observations (e.g., vegetation optical depth, lidar) can quantify the stresses from climate change and disturbances on the physical environment and ecological responses of these ecosystems. This will provide a better understanding of vegetation’s role in the Earth system and its resilience to environmental threats.

In this Special Issue of Remote Sensing, we welcome research focusing on spatio-temporal observations of ecosystems from airborne or spaceborne sensors, with particular attention paid to the extreme climate and disturbance events in recent decades. The selection of papers for publication will depend on the quality and rigor of research and results.

Prof. Dr. Lei Fan
Prof. Dr. Xiuzhi Chen
Dr. Frédéric Frappart
Prof. Dr. Yongxian Su
Dr. Yuanwei Qin
Guest Editors

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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.

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Keywords

extreme climate events
forest disturbances
resilience
recovery
fire
insect disease
drought
deforestation
degradation

Published Papers (9 papers)

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Research

14 pages, 1754 KiB

Open AccessArticle

The Influence of Burn Severity on Post-Fire Spectral Recovery of Three Fires in the Southern Rocky Mountains

by Jaclyn Guz, Florencia Sangermano and Dominik Kulakowski

Remote Sens. 2022, 14(6), 1363; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14061363 - 11 Mar 2022

Cited by 4 | Viewed by 2242

Abstract

Increased wildfire activity and altered post-fire climate in the Southern Rocky Mountains has the potential to influence forest resilience. The Southern Rocky Mountains are a leading edge of climate change and have experienced record-breaking fires in recent years. The change in post-fire regeneration and forest resilience could potentially include future ecological trajectories. In this paper, we examined patterns of post-fire spectral recovery using Landsat time series. Additionally, we utilized random forest models to analyze the impact of climate and burn severity on three fire events in the Southern Rocky Mountains. Fifteen years following the fires, none of the burned stands fully recovered to their pre-fire spectral states. The results suggested that burn severity significantly impacted post-fire spectral recovery, but that influence may decrease as time since fire increases. The biggest difference in forest recovery was among fire events, indicating that post-fire climate may be influential in post-fire recovery. The mean and minimum growing-season temperatures were more significant to post-fire recovery than the variability in precipitation, which is consistent with field-based analysis. The present study indicated that, as warming continues, there may be changes in forest density where forests are not regenerating to their pre-fire spectral states. Additionally, this study emphasizes how high-elevation forests continue to regenerate after fires, but that regeneration is markedly affected by post-fire climate. Full article

(This article belongs to the Special Issue The Impact of Extreme Climatic and Disturbance Events on Vegetation Using Remote Sensing)

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16 pages, 3647 KiB

Open AccessArticle

Monitoring the Reduced Resilience of Forests in Southwest China Using Long-Term Remote Sensing Data

by Hao Jiang, Lisheng Song, Yan Li, Mingguo Ma and Lei Fan

Remote Sens. 2022, 14(1), 32; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14010032 - 22 Dec 2021

Cited by 7 | Viewed by 3132

Abstract

An increase in the frequency and severity of droughts associated with global warming has resulted in deleterious impacts on forest productivity in Southwest China. Despite attempts to explore the response of vegetation to drought, less is known about forest’s resilience in response to drought in Southwest China. Here, the reduced resilience of the forest was found based on remotely sensed optical and microwave vegetation products. The spatial distribution and temporal variation of resilience-reduced forest were assessed using the standardized precipitation evapotranspiration index (SPEI) and vegetation optical depth (VOD). Our findings showed that 40–50% of the forest appeared to have abnormally low resilience approximately 6 months after the severe drought. The spatial distributions of abnormally low resilience had a good agreement with the regions affected by the 2009–2011 drought events. In particular, our results indicated that areas of afforestation were more susceptible to drought than natural forest, maybe due to the different water uptake strategy of the diverse root systems. Our findings highlight the vulnerability of afforestation areas to climate change, and recommend giving more attention to soil water availability. Full article

(This article belongs to the Special Issue The Impact of Extreme Climatic and Disturbance Events on Vegetation Using Remote Sensing)

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14 pages, 5840 KiB

Open AccessArticle

Investigation of Vegetation Changes in Different Mining Areas in Liaoning Province, China, Using Multisource Remote Sensing Data

by Baodong Ma, Xiangru Yang, Yajiao Yu, Yang Shu and Defu Che

Remote Sens. 2021, 13(24), 5168; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13245168 - 20 Dec 2021

Cited by 8 | Viewed by 2922

Abstract

Mining can provide necessary mineral resources for humans. However, mining activities may cause damage to the surrounding ecology and environment. Vegetation change analysis is a key tool for evaluating damage to ecology and the environment. Liaoning is one of the major mining provinces in China, with rich mineral resources and long-term, high-intensity mining activities. Taking Liaoning Province as an example, vegetation change in six mining areas was investigated using multisource remote sensing data to evaluate ecological and environmental changes. Based on MODIS NDVI series data from 2000 to 2019, change trends of vegetation were evaluated using linear regression. According to the results, there are large highly degraded vegetation areas in the Anshan, Benxi, and Yingkou mining areas, which indicates that mining activities have seriously damaged the vegetation in these areas. In contrast, there are considerable areas with improved vegetation in the Anshan, Fushun, and Fuxin mining areas, which indicates that ecological reclamation has played a positive role in these areas. Based on Sentinel-2A data, leaf chlorophyll content was inferred by using the vegetation index MERIS Terrestrial Chlorophyll Index (MTCI) after measurement of leaf spectra and chlorophyll content were carried out on the ground to validate the performance of MTCI. According to the results, the leaf chlorophyll content in the mines is generally lower than in adjacent areas in these mining areas with individual differences. In the Yingkou mining area, the chlorophyll content in adjacent areas is close to the magnesite mines, which means the spillover effect of environmental pollution in mines should be considerable. In the Anshan, Benxi, and Diaobingshan mining areas, the environmental stress on adjacent areas is slight. All in all, iron and magnesite open-pit mines should be monitored closely for vegetation destruction and stress due to the high intensity of mining activities and serious pollution. In contrast, the disturbance to vegetation is limited in resource-exhausted open-pit coal mines and underground coal mines. It is suggested that land reclamation should be enhanced to improve the vegetation in active open-pit mining areas, such as the Anshan, Benxi, and Yingkou mining areas. Additionally, environmental protection measures should be enhanced to relieve vegetation stress in the Yingkou mining area. Full article

(This article belongs to the Special Issue The Impact of Extreme Climatic and Disturbance Events on Vegetation Using Remote Sensing)

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18 pages, 59973 KiB

Open AccessArticle

Estimating Gross Primary Productivity (GPP) over Rice–Wheat-Rotation Croplands by Using the Random Forest Model and Eddy Covariance Measurements: Upscaling and Comparison with the MODIS Product

by Zexia Duan, Yuanjian Yang, Shaohui Zhou, Zhiqiu Gao, Lian Zong, Sihui Fan and Jian Yin

Remote Sens. 2021, 13(21), 4229; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13214229 - 21 Oct 2021

Cited by 9 | Viewed by 3586

Abstract

Despite advances in remote sensing–based gross primary productivity (GPP) modeling, the calibration of the Moderate Resolution Imaging Spectroradiometer (MODIS) GPP product (GPP_MOD) is less well understood over rice–wheat-rotation cropland. To improve the performance of GPP_MOD, a random forest (RF) machine learning model was constructed and employed over the rice–wheat double-cropping fields of eastern China. The RF-derived GPP (GPP_RF) agreed well with the eddy covariance (EC)-derived GPP (GPP_EC), with a coefficient of determination of 0.99 and a root-mean-square error of 0.42 g C m⁻² d⁻¹. Therefore, it was deemed reliable to upscale GPP_EC to regional scales through the RF model. The upscaled cumulative seasonal GPP_RF was higher for rice (924 g C m⁻²) than that for wheat (532 g C m⁻²). By comparing GPP_MOD and GPP_EC, we found that GPP_MOD performed well during the crop rotation periods but underestimated GPP during the rice/wheat active growth seasons. Furthermore, GPP_MOD was calibrated by GPP_RF, and the error range of GPP_MOD (GPP_RF minus GPP_MOD) was found to be 2.5–3.25 g C m⁻² d⁻¹ for rice and 0.75–1.25 g C m⁻² d⁻¹ for wheat. Our findings suggest that RF-based GPP products have the potential to be applied in accurately evaluating MODIS-based agroecosystem carbon cycles at regional or even global scales. Full article

(This article belongs to the Special Issue The Impact of Extreme Climatic and Disturbance Events on Vegetation Using Remote Sensing)

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15 pages, 6615 KiB

Open AccessArticle

The Ongoing Greening in Southwest China despite Severe Droughts and Drying Trends

by Xin Chen, Tiexi Chen, Qingyun Yan, Jiangtao Cai, Renjie Guo, Miaoni Gao, Xueqiong Wei, Shengjie Zhou, Chaofan Li and Yong Xie

Remote Sens. 2021, 13(17), 3374; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13173374 - 25 Aug 2021

Cited by 7 | Viewed by 2986

Abstract

Vegetation greening, which refers to the interannual increasing trends of vegetation greenness, has been widely found on the regional to global scale. Meanwhile, climate extremes, especially several drought, significantly damage vegetation. The Southwest China (SWC) region experienced massive drought from 2009 to 2012, which severely damaged vegetation and had a huge impact on agricultural systems and life. However, whether these extremes have significantly influenced long-term (multiple decades) vegetation change is unclear. Using the latest remote sensing-based records, including leaf area index (LAI) and gross primary productivity (GPP) for 1982–2016 and enhanced vegetation index (EVI) for 2001–2019, drought events of 2009–2012 only leveled off the greening (increasing in vegetation indices and GPP) temporally and long-term greening was maintained. Meanwhile, drying trends were found to unexpectedly coexist with greening. Full article

(This article belongs to the Special Issue The Impact of Extreme Climatic and Disturbance Events on Vegetation Using Remote Sensing)

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20 pages, 5096 KiB

Open AccessArticle

New Forest Aboveground Biomass Maps of China Integrating Multiple Datasets

by Zhongbing Chang, Sanaa Hobeichi, Ying-Ping Wang, Xuli Tang, Gab Abramowitz, Yang Chen, Nannan Cao, Mengxiao Yu, Huabing Huang, Guoyi Zhou, Genxu Wang, Keping Ma, Sheng Du, Shenggong Li, Shijie Han, Youxin Ma, Jean-Pierre Wigneron, Lei Fan, Sassan S. Saatchi and Junhua Yan

Remote Sens. 2021, 13(15), 2892; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13152892 - 23 Jul 2021

Cited by 11 | Viewed by 5130

Abstract

Mapping the spatial variation of forest aboveground biomass (AGB) at the national or regional scale is important for estimating carbon emissions and removals and contributing to global stocktake and balancing the carbon budget. Recently, several gridded forest AGB products have been produced for China by integrating remote sensing data and field measurements, yet significant discrepancies remain among these products in their estimated AGB carbon, varying from 5.04 to 9.81 Pg C. To reduce this uncertainty, here, we first compiled independent, high-quality field measurements of AGB using a systematic and consistent protocol across China from 2011 to 2015. We applied two different approaches, an optimal weighting technique (WT) and a random forest regression method (RF), to develop two observationally constrained hybrid forest AGB products in China by integrating five existing AGB products. The WT method uses a linear combination of the five existing AGB products with weightings that minimize biases with respect to the field measurements, and the RF method uses decision trees to predict a hybrid AGB map by minimizing the bias and variance with respect to the field measurements. The forest AGB stock in China was 7.73 Pg C for the WT estimates and 8.13 Pg C for the RF estimates. Evaluation with the field measurements showed that the two hybrid AGB products had a lower RMSE (29.6 and 24.3 Mg/ha) and bias (−4.6 and −3.8 Mg/ha) than all five participating AGB datasets. Our study demonstrated both the WT and RF methods can be used to harmonize existing AGB maps with field measurements to improve the spatial variability and reduce the uncertainty of carbon stocks. The new spatial AGB maps of China can be used to improve estimates of carbon emissions and removals at the national and subnational scales. Full article

(This article belongs to the Special Issue The Impact of Extreme Climatic and Disturbance Events on Vegetation Using Remote Sensing)

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23 pages, 9844 KiB

Open AccessArticle

Vegetation Change and Its Response to Climate Extremes in the Arid Region of Northwest China

by Simeng Wang, Qihang Liu and Chang Huang

Remote Sens. 2021, 13(7), 1230; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13071230 - 24 Mar 2021

Cited by 25 | Viewed by 3412

Abstract

Changes in climate extremes have a profound impact on vegetation growth. In this study, we employed the Moderate Resolution Imaging Spectroradiometer (MODIS) and a recently published climate extremes dataset (HadEX3) to study the temporal and spatial evolution of vegetation cover, and its responses to climate extremes in the arid region of northwest China (ARNC). Mann-Kendall test, Anomaly analysis, Pearson correlation analysis, Time lag cross-correlation method, and Least absolute shrinkage and selection operator logistic regression (Lasso) were conducted to quantitatively analyze the response characteristics between Normalized Difference Vegetation Index (NDVI) and climate extremes from 2000 to 2018. The results showed that: (1) The vegetation in the ARNC had a fluctuating upward trend, with vegetation significantly increasing in Xinjiang Tianshan, Altai Mountain, and Tarim Basin, and decreasing in the central inland desert. (2) Temperature extremes showed an increasing trend, with extremely high-temperature events increasing and extremely low-temperature events decreasing. Precipitation extremes events also exhibited a slightly increasing trend. (3) NDVI was overall positively correlated with the climate extremes indices (CEIs), although both positive and negative correlations spatially coexisted. (4) The responses of NDVI and climate extremes showed time lag effects and spatial differences in the growing period. (5) Precipitation extremes were closely related to NDVI than temperature extremes according to Lasso modeling results. This study provides a reference for understanding vegetation variations and their response to climate extremes in arid regions. Full article

(This article belongs to the Special Issue The Impact of Extreme Climatic and Disturbance Events on Vegetation Using Remote Sensing)

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16 pages, 3730 KiB

Open AccessArticle

Spatiotemporal Patterns of Ecosystem Restoration Activities and Their Effects on Changes in Terrestrial Gross Primary Production in Southwest China

by Zhi Ding, Hui Zheng, Ying Liu, Sidong Zeng, Pujia Yu, Wei Shi and Xuguang Tang

Remote Sens. 2021, 13(6), 1209; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13061209 - 23 Mar 2021

Cited by 5 | Viewed by 2244

Abstract

Large-scale ecosystem restoration projects (ERPs) have been implemented since the beginning of the new millennium to restore vegetation and improve the ecosystem in Southwest China. However, quantifying the effects of specific restoration activities, such as afforestation and grass planting, on vegetation recovery is difficult due to their incommensurable spatiotemporal distribution. Long-term and successive ERP-driven land use/cover changes (LUCCs) were used to recognise the spatiotemporal patterns of major restoration activities, and a contribution index was defined to assess the effects of these activities on gross primary production (GPP) dynamics in Southwest China during the period of 2001–2015. The results were as follows. (1) Afforestation and grass planting were major restoration activities that accounted for more than 54% of all LUCCs in Southwest China. Approximately 96% of restoration activities involved afforestation, and these activities were mostly distributed around Yunnan Province. (2) The Breathing Earth System Simulator (BESS) GPP performed better than the Moderate Resolution Imaging Spectroradiometer (MODIS) GPP validated by field observation data. Nevertheless, their annual GPP trends were similar and increased by 12,581 g C m⁻² d⁻¹ and 13,406 g C m⁻² d⁻¹ for MODIS and BESS GPPs, respectively. (3) Although the afforestation and grass planting areas accounted for less than 1% of the total area of Southwest China, they contributed to more than 1% of the annual GPP increase in the entire study area. Afforestation directly contributed 14.94% (BESS GPP) or 24.64% (MODIS GPP) to the annual GPP increase. Meanwhile, grass planting directly contributed only 0.41% (BESS GPP) or 0.03% (MODIS GPP) to the annual GPP increase. Full article

(This article belongs to the Special Issue The Impact of Extreme Climatic and Disturbance Events on Vegetation Using Remote Sensing)

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16 pages, 16283 KiB

Open AccessArticle

Detecting Forest Degradation in the Three-North Forest Shelterbelt in China from Multi-Scale Satellite Images

by Tao Yu, Pengju Liu, Qiang Zhang, Yi Ren and Jingning Yao

Remote Sens. 2021, 13(6), 1131; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13061131 - 16 Mar 2021

Cited by 17 | Viewed by 3442

Abstract

Detecting forest degradation from satellite observation data is of great significance in revealing the process of decreasing forest quality and giving a better understanding of regional or global carbon emissions and their feedbacks with climate changes. In this paper, a quick and applicable approach was developed for monitoring forest degradation in the Three-North Forest Shelterbelt in China from multi-scale remote sensing data. Firstly, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Ratio Vegetation Index (RVI), Leaf Area Index (LAI), Fraction of Photosynthetically Active Radiation (FPAR) and Net Primary Production (NPP) from remote sensing data were selected as the indicators to describe forest degradation. Then multi-scale forest degradation maps were obtained by adopting a new classification method using time series MODerate Resolution Imaging Spectroradiometer (MODIS) and Landsat Enhanced Thematic Mapper Plus (ETM+) images, and were validated with ground survey data. At last, the criteria and indicators for monitoring forest degradation from remote sensing data were discussed, and the uncertainly of the method was analyzed. Results of this paper indicated that multi-scale remote sensing data have great potential in detecting regional forest degradation. Full article

(This article belongs to the Special Issue The Impact of Extreme Climatic and Disturbance Events on Vegetation Using Remote Sensing)

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