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Remote Sensing of Vegetation Dynamics and Resilience

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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 June 2021) | Viewed by 30215

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Special Issue Editors

Dr. Célia Marina P. Gouveia

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Faculdade de Ciências, Universidade de Lisboa, Campo Grande, Edf. C8, Piso 3, Sala 8.3.03, 1749-016 Lisboa, Portugal
Interests: climate variability and extremes; droughts; heatwaves; vegetation dynamics; remote sensing; wildfires

Dr. Renata Libonati

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Guest Editor

Instituto de Geociências (IGEO), Universidade Federal do Rio de Janeiro, UFRJ, Rio de Janeiro 21941-916, Brazil
Interests: climate; remote sensing; wildfires; climate extremes such as drought and heatwaves
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Special Issue Information

Dear Colleagues,

Over the last four decades, continuous monitoring of vegetation from space has allowed a deeper understanding of the links between the observed major global changes in vegetation dynamics and climate (e.g., trends, variability and extremes). The recent availability of reliable remote sensing data covering wide regions over long periods of time has progressively strengthened its role in environmental studies, in particular in those related to climate extremes.

Extreme climate events, such as heatwaves, droughts or wildfires, are expected to become more intense, frequent and long lasting worldwide. In our days, the response of land vegetation to extreme events is still a challenging subject, as growth and CO2 uptake by plants are constrained to a large extent by droughts and heatwaves. Differences in the physiological response of plant species to water scarcity and high evaporative demand determine different levels of resistance and resilience and ultimately determining a slow growth or reduce greenness, leading to loss of biomass and plant mortality.

Special attention has been devoted to long-term, large-scale studies aiming to assess the impact of atmospheric circulation variability on surface climate and related vegetation activity. Global and regional patterns of teleconnections have pointed to explain land ecosystems processes even better than single climate variables, because they influence simultaneously the range of weather variables that cause interacting, and sometimes opposing, responses by ecosystems.

This Special Issue intends to be a useful and valuable demonstration of the added value of using remote sensing for vegetation dynamics studies worldwide, and a mean of stimulating multidisciplinary collaborations for land ecosystems dynamics understanding. Therefore, the submission of articles regarding, but not limited to, the following topics associated to remote sensing techniques:

Long-term studies of vegetation dynamics
Vegetation dynamics and associated drivers at global and regional scales
Vegetation dynamics in the context of climate change, extremes, and variability
Vegetation dynamics and carbon uptake
Vegetation dynamics in drylands and in humid ecosystems
Global Trends in vegetation dynamics
Groundwater dependent ecosystems
Fire and vegetation dynamics
Vegetation resilience of land ecosystems
Land degradation and desertification
Land cover change and use and vegetation dynamics
Vegetation resilience in fire and drought-prone regions
Innovative use of remote sensing technologies for vegetation characterization and monitoring, as well as, change detection.

Dr. Célia Gouveia
Dr. Renata Libonati
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

Extreme climate events
Vegetation dynamics
Droughts
Fires
Remote sensing
Climate variability and change
Vegetation resilience
Land degradation
Carbon balance

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Published Papers (7 papers)

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Research

18 pages, 5587 KiB

Open AccessArticle

Multi-Sensor, Active Fire-Supervised, One-Class Burned Area Mapping in the Brazilian Savanna

by Allan A. Pereira, Renata Libonati, Julia A. Rodrigues, Joana Nogueira, Filippe L. M. Santos, Duarte Oom, Waislan Sanches, Swanni T. Alvarado and José M. C. Pereira

Remote Sens. 2021, 13(19), 4005; https://doi.org/10.3390/rs13194005 - 6 Oct 2021

Cited by 8 | Viewed by 2631

Abstract

Increasing efforts are being devoted to understanding fire patterns and changes highlighting the need for a consistent database about the location and extension of burned areas (BA). Satellite-derived BA mapping accuracy in the Brazilian savannas is limited by the underestimation of burn scars from small, fragmented fires and high cloudiness. Moreover, systematic mapping of BA is challenged by the need for human intervention in training sample acquisition, which precludes the development of automatic-generated products over large areas and long periods. Here, we developed a multi-sensor, active fire-supervised, one-class BA mapping algorithm to address several of these limitations. Our main objective is to generate a long-term, detailed BA atlas suitable to improve fire regime characterization and validation of coarse resolution products. We use composite images derived from the Landsat satellite to generate end-of-season maps of fire-affected areas for the entire Cerrado. Validation exercises and intercomparison with BA maps from a semi-automatic algorithm and visual photo interpretation were conducted for the year 2015. Our results improve the BA mapping by reducing omission errors, especially where there is high cloud frequency, few active fires are detected, and burned areas are small and fragmented. Finally, our approach represents at least a 45% increase in BA mapped in the Cerrado, in comparison to the annual extent detected by the current coarse global product from MODIS satellite (MCD64), and thus, it is capable of supporting improved regional emissions estimates. Full article

(This article belongs to the Special Issue Remote Sensing of Vegetation Dynamics and Resilience)

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17 pages, 18415 KiB

Open AccessArticle

Quantifying Forest Fire and Post-Fire Vegetation Recovery in the Daxin’anling Area of Northeastern China Using Landsat Time-Series Data and Machine Learning

by Jie Qiu, Heng Wang, Wenjuan Shen, Yali Zhang, Huiyi Su and Mingshi Li

Remote Sens. 2021, 13(4), 792; https://doi.org/10.3390/rs13040792 - 21 Feb 2021

Cited by 32 | Viewed by 4685

Abstract

Many post-fire on-site factors, including fire severity, management strategies, topography, and local climate, are concerns for forest managers and recovery ecologists to formulate forest vegetation recovery plans in response to climate change. We used the Vegetation Change Tracker (VCT) algorithm to map forest disturbance in the Daxing’anling area, Northeastern China, from 1987 to 2016. A support vector machine (SVM) classifier and historical fire records were used to separate burned patches from disturbance patches obtained from VCT. Afterward, stepwise multiple linear regression (SMLR), SVM, and random forest (RF) were applied to assess the statistical relationships between vegetation recovery characteristics and various influential factors. The results indicated that the forest disturbance events obtained from VCT had high spatial accuracy, ranging from 70% to 86% for most years. The overall accuracy of the annual fire patches extracted from the proposed VCT-SVM algorithm was over 92%. The modeling accuracy of post-fire vegetation recovery was excellent, and the validation results confirmed that the RF algorithm provided better prediction accuracy than SVM and SMLR. In conclusion, topographic variables (e.g., elevation) and meteorological variables (e.g., the post-fire annual precipitation in the second year, the post-fire average relative humidity in the fifth year, and the post-fire extreme maximum temperature in the third year) jointly affect vegetation recovery in this cold temperate continental monsoon climate region. Full article

(This article belongs to the Special Issue Remote Sensing of Vegetation Dynamics and Resilience)

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22 pages, 17456 KiB

Open AccessArticle

Spatiotemporal Variation of Vegetation on the Qinghai–Tibet Plateau and the Influence of Climatic Factors and Human Activities on Vegetation Trend (2000–2019)

by Junhan Chen, Feng Yan and Qi Lu

Remote Sens. 2020, 12(19), 3150; https://doi.org/10.3390/rs12193150 - 25 Sep 2020

Cited by 100 | Viewed by 5504

Abstract

Vegetation is the terrestrial ecosystem component most sensitive to climate change. The Qinghai–Tibet Plateau (QTP), characterized by a cold climate and vulnerable ecosystems, has experienced significant warming in previous decades. Identifying the variation in vegetation coverage and elucidating its main driving factors are critical for ecological protection on the QTP. In this study, MOD13A2 Normalized Difference Vegetation Index (NDVI) data in the growing season (May to September) was used to represent QTP vegetation coverage during 2000–2019. The univariate linear regression, partial correlation analysis, residual analysis, and the Hurst exponent were used to detect the vegetation spatiotemporal dynamic, analyze the relationship between the vegetation and main driving factors, and predict the future vegetation dynamic. The growing season NDVI (GNDVI) of the QTP showed an extremely significant rate of increase (0.0011/a) during the study period, and 79.29% of the vegetated areas showed a greening trend. Over the past 20 years, the northeast, mid-east, and western edges of the plateau have been cooling and wetting, while the southwest, mid-west, and southeast have been warming and drying. Different climatic conditions lead to spatial differences in the response of plateau vegetation to climatic factors with generally 1–4 months lag time. The vegetation in the north of the plateau was mainly positively correlated with moisture, and negatively correlated with temperature, while the southern part showed positive correlation with temperature and negative correlation with moisture. Due to the enhancement of cooling and wetting trend in the last decade (2010–2019), especially in the south of the plateau, the greening trend of the plateau vegetation slowed down appreciably and even degraded in some areas. Human activities were mainly concentrated in the eastern part of the plateau—and its positive effect on vegetation was gradually increasing in most areas during study period, especially in the northeastern part. However, vegetation degradation caused by human activities in the southeast of the plateau should not be ignored. The future vegetation dynamic based on the Hurst exponent showed that the plateau faces a higher risk of vegetation degradation, which deserves more attention. This study explored the effect of climatic factors and human activities on vegetation of the QTP, thereby providing some guidance for the study of vegetation dynamic in the alpine areas. Full article

(This article belongs to the Special Issue Remote Sensing of Vegetation Dynamics and Resilience)

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18 pages, 5771 KiB

Open AccessArticle

Fully Automated Countrywide Monitoring of Fuel Break Maintenance Operations

by Valentine Aubard, João E. Pereira-Pires, Manuel L. Campagnolo, José M. C. Pereira, André Mora and João M. N. Silva

Remote Sens. 2020, 12(18), 2879; https://doi.org/10.3390/rs12182879 - 5 Sep 2020

Cited by 8 | Viewed by 3318

Abstract

Fuel break (FB) networks are strategic locations for fire control and suppression. In order to be effective for wildfire control, they need to be maintained through regular interventions to reduce fuel loads. In this paper, we describe a monitoring system relying on Earth observations to detect fuel reduction inside the FB network being implemented in Portugal. Two fast automated pixel-based methodologies for monthly monitoring of fuel removals in FB are developed and compared. The first method (M1) is a classical supervised classification using the difference and postdisturbance image of monthly image composites. To take into account the impact of different land cover and phenology in the detection of fuel treatments, a second method (M2) based on an innovative statistical change detection approach was developed. M2 explores time series of vegetation indices and does not require training data or user-defined thresholds. The two algorithms were applied to Sentinel-2 10 m bands and fully processed in the cloud-based platform Google Earth Engine. Overall, the unsupervised M2, which is based on a Welch t-test of two moving window averages, gives better results than the supervised M1 and is suitable for an automated countrywide fuel treatment detection. For both methods, two vegetation indices, the Modified Excess of Green and the Normalized Difference Vegetation Index, were compared and exhibited similar performances. Full article

(This article belongs to the Special Issue Remote Sensing of Vegetation Dynamics and Resilience)

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21 pages, 9117 KiB

Open AccessArticle

Driving Factors of Recent Vegetation Changes in Hexi Region, Northwest China Based on a New Classification Framework

by Ju Wang, Yaowen Xie, Xiaoyun Wang and Kunming Guo

Remote Sens. 2020, 12(11), 1758; https://doi.org/10.3390/rs12111758 - 29 May 2020

Cited by 19 | Viewed by 2451

Abstract

Since other factors (soil properties, topography, etc.) under natural conditions are relatively invariant over one or two decades, climate variables (precipitation and temperature) and human activities are the two fundamental factors driving vegetation changes in global or large-scale areas. However, the combined effects of either single climatic factor and human activities on vegetation changes and the role of human activities itself in a specific region has not been fully discussed. In this study, the Hexi region, a typical dryland consisting of three inland river basins in northwest China was selected as a case area. A new classification framework combining Pearson correlation analysis and residual trend approach was proposed to assess their individual and conjoint contributions of climate variables and human activities in areas of significant vegetation changes. Our results indicated that most of vegetation covered areas in the Hexi region experienced significant changes during the period 2001−2017, and vegetation improvements were widespread except the interior of oases; significant changes in vegetation caused by human activities, precipitation, the interactions of precipitation and human activities, temperature, the interactions of temperature and human activities, the interactions of temperature and precipitation, and the interactions of the three factors accounted for 50.46%, 16.39%, 19.90%, 4.33%, 2.32%, 2.11%, and 4.49% of the total change areas, respectively. Generally, the influence of temperature was relatively weaker than that of precipitation, and the contributions of the interactions of climate variables and human activities on vegetation changes were greater than that of climate contributions alone. Moreover, the results of various investigations, according to the trends and the time of vegetation changes, indicate that decreasing trends of the normalized difference vegetation index (NDVI) in the Hexi region were chiefly attributed to the adjustments of agricultural planting structure while the comprehensive treatment programs implemented in river basins supported a large proportion of vegetation improvements. Full article

(This article belongs to the Special Issue Remote Sensing of Vegetation Dynamics and Resilience)

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29 pages, 15001 KiB

Open AccessArticle

Quantifying the Impacts of Anthropogenic Activities and Climate Variations on Vegetation Productivity Changes in China from 1985 to 2015

by Shahid Naeem, Yongqiang Zhang, Jing Tian, Faisal Mueen Qamer, Aamir Latif and Pranesh Kumar Paul

Remote Sens. 2020, 12(7), 1113; https://doi.org/10.3390/rs12071113 - 31 Mar 2020

Cited by 51 | Viewed by 6486

Abstract

Accurate assessment of vegetation dynamics provides important information for ecosystem management. Anthropogenic activities and climate variations are the major factors that primarily influence vegetation ecosystems. This study investigates the spatiotemporal impacts of climate factors and human activities on vegetation productivity changes in China from 1985 to 2015. Actual net primary productivity (ANPP) is used to reflect vegetation dynamics quantitatively. Climate-induced potential net primary productivity (PNPP) is used as an indicator of climate change, whereas the difference between PNPP and ANPP is considered as an indicator of human activities (HNPP). Overall, 91% of the total vegetation cover area shows declining trends for net primary productivity (NPP), while only 9% shows increasing trends before 2000 (base period). However, after 2000 (restoration period), 78.7% of the total vegetation cover area shows increasing trends, whereas 21.3% of the area shows decreasing trends. Moreover, during the base period, the quantitative contribution of climate change to NPP restoration is 0.21 grams carbon per meter square per year (gC m⁻² yr⁻¹) and to degradation is 2.41 gC m⁻² yr⁻¹, while during the restoration period, climate change contributes 0.56 and 0.29 gC m⁻² yr⁻¹ to NPP restoration and degradation, respectively. Human activities contribute 0.36 and 0.72 gC m⁻² yr⁻¹ during the base period, and 0.63 and 0.31 gC m⁻² yr⁻¹ during the restoration period to NPP restoration and degradation, respectively. The combined effects of climate and human activities restore 0.65 and 1.11 gC m⁻² yr⁻¹, and degrade 2.01 and 0.67 gC m⁻² yr⁻¹ during the base and restoration periods, respectively. Climate factors affect vegetation cover more than human activities, while precipitation is found to be more sensitive to NPP change than temperature. Unlike the base period, NPP per unit area increases with an increase in the human footprint pressure during the restoration period. Grassland has more variability than other vegetation classes, and the grassland changes are mainly observed in Tibet, Xinjiang, and Inner Mongolia regions. The results may help policy-makers by providing necessary guidelines for the management of forest, grassland, and agricultural activities. Full article

(This article belongs to the Special Issue Remote Sensing of Vegetation Dynamics and Resilience)

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24 pages, 6661 KiB

Open AccessArticle

Detecting Patterns of Vegetation Gradual Changes (2001–2017) in Shiyang River Basin, Based on a Novel Framework

by Ju Wang, Yaowen Xie, Xiaoyun Wang, Jingru Dong and Qiang Bie

Remote Sens. 2019, 11(21), 2475; https://doi.org/10.3390/rs11212475 - 24 Oct 2019

Cited by 17 | Viewed by 3556

Abstract

A lot of timeseries satellite products have been well documented in exploring changes in ecosystems. However, algorithms allowing for measuring the directions, magnitudes, and timing of vegetation change, evaluating the major driving factors, and eventually predicting the future trends are still insufficient. A novel framework focusing on addressing this problem was proposed in this study according to the temporal trajectory of Normalized Difference Vegetation Index (NDVI) timeseries of Moderate Resolution Imaging Spectroradiometer (MODIS). It divided the inter-annual changes in vegetation into four patterns: linear, exponential, logarithmic, and logistic. All the three non-linear patterns were differentiated automatically by fitting a logistic function with prolonged NDVI timeseries. Finally, features of vegetation changes including where, when and how, were evaluated by the parameters in the logistic function. Our results showed that 87.39% of vegetation covered areas (maximum mean growing season NDVI in the 17 years not less than 0.2) in the Shiyng River basin experienced significant changes during 2001–2017. The linear pattern, exponential pattern, logarithmic pattern, and logistic pattern accounted for 36.53%, 20.16%, 15.42%, and 15.27%, respectively. Increasing trends were dominant in all the patterns. The spatial distribution in both the patterns and the transition years at which vegetation gains/losses began or ended is of high consistency. The main years of transition for the exponential increasing pattern, the logarithmic increasing pattern, and the logarithmic increasing pattern were 2008–2011, 2003–2004, and 2009–2010, respectively. The period of 2006–2008 was the foremost period that NDVIs started to decline in Liangzhou Oasis and Minqin Oasis where almost all the decreasing patterns were concentrated. Potential disturbances of vegetation gradual changes in the basin are refer to as urbanization, expansion or reduction of agricultural oases, as well as measures in ecological projects, such as greenhouses building, afforestation, grazing prohibition, etc. Full article

(This article belongs to the Special Issue Remote Sensing of Vegetation Dynamics and Resilience)

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