Submit to Remote Sensing Review for Remote Sensing Propose a Special Issue

Journal Browser

Remote Sensing of Vegetation: Mapping, Trend Analysis, and Drivers of Change

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Published Papers

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

Deadline for manuscript submissions: closed (1 March 2024) | Viewed by 17636

Share This Special Issue

Special Issue Editors

Dr. Sadegh Jamali

E-Mail Website
Guest Editor

Department of Technology and Society, Lund University, SE-221 00 Lund, Sweden
Interests: vegetation mapping; time-series analysis; change detection; machine learning; earth observation; remote sensing

Dr. Torbern Tagesson

E-Mail Website
Guest Editor

1. Department of Physical Geography and Ecosystem Science, Lund University, SE-223 62 Lund, Sweden
2. Department of Geosciences and Natural Resource Management, University of Copenhagen, DK-1350 Copenhagen, Denmark
Interests: land-atmosphere interactions; global carbon cycling; plant physiology; vegetation productivity; time series analysis; micrometeorology; earth observations; remote sensing; biogeochemical cycling

Dr. Feng Tian

E-Mail Website
Guest Editor

School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430000, China
Interests: vegetation phenology; vegetation productivity; vegetation optical depth; optical vegetation indices; eddy covariance; plant physiology; climate change

Dr. Meisam Amani

E-Mail Website
Guest Editor

Wood Environment & Infrastructure Solutions, 210 Colonnade Road, Ottawa, ON K2E 7L5, Canada
Interests: remote sensing; wetlands; met-ocean; classification; machine learning; big data
Special Issues, Collections and Topics in MDPI journals

Dr. Per-Ola Olsson

E-Mail Website
Guest Editor

Department of Physical Geography and Ecosystem Science, Lund University, 223 62 Lund, Sweden
Interests: disturbance monitoring; time-series analysis; vegetation mapping; machine learning

Dr. Arsalan Ghorbanian

E-Mail Website
Guest Editor

1. Department of Photogrammetry and Remote Sensing, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi of University of Technology, Tehran 19967-15433, Iran
2. Department of Technology and Society, Lund University, 221 00 Lund, Sweden
Interests: remote sensing; land cover mapping; Google Earth Engine (GEE); Big Geo Data
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Vegetation is a vital component of the Earth’s system as it is involved in many interactions between the biosphere, atmosphere, hydrosphere, and lithosphere. More particularly, vegetation plays a key role in Earth’s biogeochemical cycles and surface energy balance, converting solar energy to biomass to support the food chain, oxygen production and carbon sequestration, soil development and erosion prevention, heat control, and many other benefits to the humans and environment. Accordingly, mapping vegetation dynamics is of significant importance for many interdisciplinary/multidisciplinary studies and decision-making that directly or indirectly support the United Nations SDGs. Furthermore, time-series monitoring allows a deepening in our understanding of vegetation response to anthropogenic activities and natural processes in a climate change perspective.

Over the last decades, remote sensing advances in conjunction with statistical and machine learning algorithms and powerful cloud computing platforms have enabled efficient mapping and monitoring of the vegetation. The possibility of acquiring remote sensing data from different sensor sources (e.g., multispectral, SAR, LiDAR, and Thermal) and with different spatial, temporal, and radiometric characteristics has created unprecedented opportunities to study vegetation dynamics. Furthermore, the availability of time-series of remote sensing data enables us to uncover the driving mechanism of changes in the vegetation cover.

The forthcoming Special Issue (SI) welcomes all types of manuscripts with an added value of using time-series remote sensing data in all aspects regarding mapping, change detection, trend analysis, and studies of drivers of vegetation change in all ecosystems. This SI solicits review and original papers addressing traditional, up-to-date, and prospects of vegetation studies using local or cloud computing of remote sensing. The potential topics include but are not limited to:

Statistical and machine learning algorithms for mapping, monitoring, and trend analysis of the vegetation
Vegetation mapping (i.e., fraction, species, diversity) in different ecosystems (e.g., terrestrial, aquatic, mountainous, wetlands)
Seasonal/annual/decadal change detection and trend analysis of vegetation
Vegetation dynamics and association to carbon storage, desertification, and land degradation
Vegetation dynamics in urban areas (urban greening or loss)
Monitoring of extreme vegetation disturbances and post-event recovery
Retrieving time-series of biophysical parameters for vegetation monitoring
Response of vegetation dynamics to climatic variables change (temperature, precipitation, etc.)
Investigating the driving mechanism of vegetation change due to human activities and/or natural phenomena (e.g., climate change, drought)

Dr. Sadegh Jamali
Dr. Torbern Tagesson
Dr. Feng Tian
Dr. Meisam Amani
Dr. Per-Ola Olsson
Dr. Arsalan Ghorbanian
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 dynamics
vegetation mapping
time-series analysis
change detection
change drivers
machine learning
cloud computing
climate change

Published Papers (11 papers)

Download All Papers

Research

19 pages, 7184 KiB

Open AccessArticle

Monitoring of Plant Ecological Units Cover Dynamics in a Semiarid Landscape from Past to Future Using Multi-Layer Perceptron and Markov Chain Model

by Masoumeh Aghababaei, Ataollah Ebrahimi, Ali Asghar Naghipour, Esmaeil Asadi and Jochem Verrelst

Remote Sens. 2024, 16(9), 1612; https://doi.org/10.3390/rs16091612 - 30 Apr 2024

Viewed by 305

Abstract

Anthropogenic activities and natural disturbances cause changes in natural ecosystems, leading to altered Plant Ecological Units (PEUs). Despite a long history of land use and land cover change detection, the creation of change detection maps of PEUs remains problematic, especially in arid and semiarid landscape. This study aimed to determine and describe the changes in PEUs patterns in the past and present, and also predict and monitor future PEUs dynamics using the multi-layer perceptron-Markov chain (MLP-MC) model in a semiarid landscape in Central Zagros, Iran. Analysis of PEUs classification maps formed the basis for the identification of the main drivers in PEUs changes. First, an optimal time-series dataset of Landsat images were selected to derive PEUs classification maps in three periods, each separated by 16 years. Then, PEUs multi-temporal maps classified for period 1 (years 1986–1988) period 2 (years 2002–2004), and period 3 (years 2018–2020) were employed to analyze and predict PEUs dynamics. The dominant transitions were identified, and the transition potential was determined by developing twelve sub-models in the final change prediction process. Transitions were modeled using a Multi-Layer Perceptron (MLP) algorithm. To predict the PEU map for period 3, two PEUs classification maps of period 1 and period 2 were used using the MLP-MC method. The classified map and the predicted map of period 3 were used to evaluate and validate the predicted results. Finally, based on the results, transitions of future PEUs were predicted for the year 2036. The MLP-MC model proved to be a powerful model that can predict future PEUs dynamics that are the result of current human and managerial activities. The findings of this study demonstrate that the impact of anthropogenic processes and management activities will become visible in the natural environment and ecosystem in less than a decade. Full article

(This article belongs to the Special Issue Remote Sensing of Vegetation: Mapping, Trend Analysis, and Drivers of Change)

► Show Figures

Figure 1

16 pages, 3986 KiB

Open AccessArticle

Examining Spatiotemporal Photosynthetic Vegetation Trends in Djibouti Using Fractional Cover Metrics in the Digital Earth Africa Open Data Cube

by Julee Wardle and Zachary Phillips

Remote Sens. 2024, 16(7), 1241; https://doi.org/10.3390/rs16071241 - 31 Mar 2024

Viewed by 539

Abstract

The Horn of Africa has sensitive, arid ecosystems, with its vegetation commonly distressed by factors such as climate change, population increase, unstable water resources, and rarely enforced land use management practices. These factors make countries such as Djibouti highly variable locations for the growth of vegetation and agricultural products, and these countries are becoming more vulnerable to food insecurity as the climate warms. The rapid growth of satellite and digital image processing technology over the last five decades has improved our ability to track long-term agricultural and vegetation changes. Data cubes are a newer approach to managing satellite imagery and studying temporal patterns. Here, we use the cloud-based Digital Earth Africa, Open Data Cube to analyze 30 years of Landsat imagery and orthomosaics. We analyze long-term trends in vegetation dynamics by comparing annual fractional cover metrics (photosynthetic vegetation, non-photosynthetic vegetation, and bare ground) to the Normalized Difference Vegetation Index. Investigating Djibouti-wide and regional vegetation trends, we provide a comparison of trends between districts and highlight a primary agricultural region in the southeast as a detailed example of vegetation change. The results of the Sen’s slope and Mann–Kendall regression analyses of the data cube suggest a significant decline in vegetation (p = 0.00002), equating to a loss of ~0.09 km² of arable land per year (roughly 2.7 km² over the 30-year period). Overall, decreases in photosynthetic vegetation and increases in both non-photosynthetic vegetation and bare soil areas indicate that the region is becoming more arid and that land cover is responding to this trend. Full article

(This article belongs to the Special Issue Remote Sensing of Vegetation: Mapping, Trend Analysis, and Drivers of Change)

► Show Figures

Figure 1

19 pages, 4603 KiB

Open AccessArticle

Multiple Greenness Indexes Revealed the Vegetation Greening during the Growing Season and Winter on the Tibetan Plateau despite Regional Variations

by Jinxia Lv, Wenwu Zhao, Ting Hua, Lihao Zhang and Paulo Pereira

Remote Sens. 2023, 15(24), 5697; https://doi.org/10.3390/rs15245697 - 12 Dec 2023

Cited by 1 | Viewed by 931

Abstract

Vegetation is an essential component of terrestrial ecosystems and supplies multiple ecosystem benefits and services. Several indices have been used to monitor changes in vegetation communities using remotely-sensed data. However, only a few studies have conducted a comparative analysis of different indices concerning vegetation greenness variation. Additionally, there have been oversights in assessing the change in greenness of evergreen woody species. In this study, we used the normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), the near-infrared reflectance of terrestrial vegetation (NIRv), and the leaf area index (LAI) data derived from MODIS data to examine spatial and temporal change in vegetation greenness in the growing season (May–September) and then evaluated the evergreen vegetation greenness change using winter (December–February) greenness using trend analysis and consistency assessment methods between 2000 and 2022 on the Tibetan Plateau, China. The results found that vegetation greenness increased in 80% of pixels during the growing season (northeastern, central-eastern, and northwestern regions). Nevertheless, a decline in the southwestern and central-southern areas was identified. Similar trends in greenness were also observed in winter in about 80% of pixels. Consistency analyses based on the four indexes showed that vegetation growth was enhanced by 29% and 30% of pixels in the growing season and winter, respectively. Further, there was relatively strong consistency among the different vegetation indexes, particularly between the NIRv and EVI. The LAI was less consistent with the other indexes. These findings emphasize the importance of selecting an appropriate index when monitoring long-term temporal trends over large spatial scales. Full article

(This article belongs to the Special Issue Remote Sensing of Vegetation: Mapping, Trend Analysis, and Drivers of Change)

► Show Figures

Graphical abstract

23 pages, 13814 KiB

Open AccessArticle

Analysis of Spatial and Temporal Changes in Vegetation Cover and Driving Forces in the Yan River Basin, Loess Plateau

by Zhilin He, Tianming Yue, Yanglong Chen, Weichen Mu, Mengfei Xi and Fen Qin

Remote Sens. 2023, 15(17), 4240; https://doi.org/10.3390/rs15174240 - 29 Aug 2023

Cited by 3 | Viewed by 1329

Abstract

The Yan River Basin of the Loess Plateau is a key region for ensuring the environmental protection and sustainable development of the Yellow River Basin. Therefore, it is essential to identify how vegetation cover has changed and determine the factors that have driven these changes. In this study, we applied a three-dimensional vegetation cover model to examine the spatiotemporal variation characteristics of vegetation cover at the watershed scale in the Yan River Basin from 2001 to 2020 and forecast future trends. Subsequently, the driving forces of fractional vegetation cover (FVC) change were quantified based on meteorological, surface, and anthropogenic factors to explore the common driving relationships among these factors. (1) The accuracy of 3DFVC is better than that of FVC in the Yanhe River Basin, where the terrain is complex. (2) The temporal change trends indicated that the vegetation cover in the Yan River Basin significantly recovered and the basin FVC increased rapidly from 2001 to 2013 (S = 0.0152/a, p < 0.01) and increased gradually from 2013 to 2020 (S = 0.0015/a). The main reason for the increase in vegetation cover was the enhanced growth of medium FVC. (3) The vegetation spatial distribution showed that the FVC values varied substantially from north to south, indicating spatial heterogeneity, and 83.9% of the area presented a trend of increasing vegetation. Furthermore, vegetation cover was predicted to improve in the future. (4) The spatial heterogeneity of FVC was mainly influenced by relative humidity and rainfall, and the spatial variations in FVC were mainly determined by climate factors. Land use and cover change variations, which are influenced by human activities, represent major factors underlying the observed spatial heterogeneity. Most interactions between driving factors showed two-way enhancement or non-linear enhancement, with relative humidity and land use patterns presenting the strongest explanatory power. This study provides a scientific basis for vegetation conservation in the Yan River Basin and contributes theoretical support for decision-making regarding ecological environmental protection in the Loess Plateau and sustainable development in the Yellow River Basin. Full article

(This article belongs to the Special Issue Remote Sensing of Vegetation: Mapping, Trend Analysis, and Drivers of Change)

► Show Figures

Graphical abstract

20 pages, 7822 KiB

Open AccessArticle

Spatial-Temporal Characteristics and Driving Forces of Aboveground Biomass in Desert Steppes of Inner Mongolia, China in the Past 20 Years

by Nitu Wu, Guixiang Liu, Deji Wuyun, Bole Yi, Wala Du and Guodong Han

Remote Sens. 2023, 15(12), 3097; https://doi.org/10.3390/rs15123097 - 13 Jun 2023

Cited by 5 | Viewed by 1221

Abstract

The desert steppe serves as a transitional zone between grasslands and deserts, and long-term monitoring of aboveground biomass (AGB) in the desert steppe is essential for understanding grassland changes. While AGB observation techniques based on multisource remote-sensing data and machine-learning algorithms have been widely applied, research on monitoring methods specifically for the desert steppe remains limited. In this study, we focused on the desert steppe of Inner Mongolia, China, as the study area and used field sampling data, MODIS data, MODIS-based vegetation indices (VI), and environmental factors (topography, climate, and soil) to compare the performance of four commonly used machine-learning algorithms: multiple linear regression (MLR), partial least-squares regression (PLS), random forest (RF), and support vector machine (SVM) in AGB estimation. Based on the optimal model, the spatial–temporal characteristics of AGB from 2000 to 2020 were calculated, and the driving forces of climate change and human activities on AGB changes were quantitatively analyzed using the random forest algorithm. The results are as follows: (1) RF demonstrated outstanding performance in terms of prediction accuracy and model robustness, making it suitable for AGB estimation in the desert steppe of Inner Mongolia; (2) VI contributed the most to the model, and no significant difference was found between soil-adjusted VIs and traditional VIs. Elevation, slope, precipitation, and temperature all had positive effects on the model; (3) from 2000 to 2020, the multiyear average AGB in the study area was 58.34 g/m², exhibiting a gradually increasing distribution pattern from the inner region to the outer region (from north to south); (4) from 2000 to 2020, the proportions of grassland with AGB slightly and significantly increasing trend in the study area were 87.08% and 5.13%, respectively, while the proportions of grassland with AGB slightly and significantly decreasing trend were 7.76% and 0.05%, respectively; and (5) over the past 20 years, climate change, particularly precipitation, has been the primary driving force behind AGB changes of the study area. This research holds reference value for improving desert steppe monitoring capabilities and the rational planning of grassland resources. Full article

(This article belongs to the Special Issue Remote Sensing of Vegetation: Mapping, Trend Analysis, and Drivers of Change)

► Show Figures

Figure 1

24 pages, 6832 KiB

Open AccessArticle

Developing Spatial and Temporal Continuous Fractional Vegetation Cover Based on Landsat and Sentinel-2 Data with a Deep Learning Approach

by Zihao Wang, Dan-Xia Song, Tao He, Jun Lu, Caiqun Wang and Dantong Zhong

Remote Sens. 2023, 15(11), 2948; https://doi.org/10.3390/rs15112948 - 5 Jun 2023

Cited by 4 | Viewed by 1713

Abstract

Fractional vegetation cover (FVC) has a significant role in indicating changes in ecosystems and is useful for simulating growth processes and modeling land surfaces. The fine-resolution FVC products represent detailed vegetation cover information within fine grids. However, the long revisit cycle of satellites with fine-resolution sensors and cloud contamination has resulted in poor spatial and temporal continuity. In this study, we propose to derive a spatially and temporally continuous FVC dataset by comparing multiple methods, including the data-fusion method (STARFM), curve-fitting reconstruction (S-G filtering), and deep learning prediction (Bi-LSTM). By combining Landsat and Sentinel-2 data, the integrated FVC was used to construct the initial input of fine-resolution FVC with gaps. The results showed that the FVC of gaps were estimated and time-series FVC was reconstructed. The Bi-LSTM method was the most effective and achieved the highest accuracy (R² = 0.857), followed by the data-fusion method (R² = 0.709) and curve-fitting method (R² = 0.705), and the optimal time step was 3. The inclusion of relevant variables in the Bi-LSTM model, including LAI, albedo, and FAPAR derived from coarse-resolution products, further reduced the RMSE from 5.022 to 2.797. By applying the optimized Bi-LSTM model to Hubei Province, a time series 30 m FVC dataset was generated, characterized by a spatial and temporal continuity. In terms of the major vegetation types in Hubei (e.g., evergreen and deciduous forests, grass, and cropland), the seasonal trends as well as the spatial details were captured by the reconstructed 30 m FVC. It was concluded that the proposed method was applicable to reconstruct the time-series FVC over a large spatial scale, and the produced fine-resolution dataset can support the data needed by many Earth system science studies. Full article

(This article belongs to the Special Issue Remote Sensing of Vegetation: Mapping, Trend Analysis, and Drivers of Change)

► Show Figures

Figure 1

18 pages, 6261 KiB

Open AccessArticle

Detection and Monitoring of Woody Vegetation Landscape Features Using Periodic Aerial Photography

by Damjan Strnad, Štefan Horvat, Domen Mongus, Danijel Ivajnšič and Štefan Kohek

Remote Sens. 2023, 15(11), 2766; https://doi.org/10.3390/rs15112766 - 26 May 2023

Cited by 1 | Viewed by 1284

Abstract

Woody vegetation landscape features, such as hedges, tree patches, and riparian vegetation, are important elements of landscape and biotic diversity. For the reason that biodiversity loss is one of the major ecological problems in the EU, it is necessary to establish efficient workflows for the registration and monitoring of woody vegetation landscape features. In the paper, we propose and evaluate a methodology for automated detection of changes in woody vegetation landscape features from a digital orthophoto (DOP). We demonstrate its ability to capture most of the actual changes in the field and thereby provide valuable support for more efficient maintenance of landscape feature layers, which is important for the shaping of future environmental policies. While the most reliable source for vegetation cover mapping is a combination of LiDAR and high-resolution imagery, it can be prohibitively expensive for continuous updates. The DOP from cyclic aerial photography presents an alternative source of up-to-date information for tracking woody vegetation landscape features in-between LiDAR recordings. The proposed methodology uses a segmentation neural network, which is trained with the latest DOP against the last known ground truth as the target. The output is a layer of detected changes, which are validated by the user before being used to update the woody vegetation landscape feature layer. The methodology was tested using the data of a typical traditional Central European cultural landscape, Goričko, in north-eastern Slovenia. The achieved

F_{1}

of per-pixel segmentation was 83.5% and 77.1% for two- and five-year differences between the LiDAR-based reference and the DOP, respectively. The validation of the proposed changes at a minimum area threshold of 100 m² and a minimum area percentage threshold of 20% showed that the model achieved recall close to 90%. Full article

(This article belongs to the Special Issue Remote Sensing of Vegetation: Mapping, Trend Analysis, and Drivers of Change)

► Show Figures

Graphical abstract

26 pages, 14568 KiB

Open AccessArticle

Comparison of Phenological Parameters Extracted from SIF, NDVI and NIRv Data on the Mongolian Plateau

by Cha Ersi, Tubuxin Bayaer, Yuhai Bao, Yulong Bao, Mei Yong, Quan Lai, Xiang Zhang and Yusi Zhang

Remote Sens. 2023, 15(1), 187; https://doi.org/10.3390/rs15010187 - 29 Dec 2022

Cited by 5 | Viewed by 1870

Abstract

The phenological parameters estimated from different data may vary, especially in response to climatic factors. Therefore, we estimated the start of the growing season (SOS) and the end of the growing season (EOS) based on sunlight-induced chlorophyll fluorescence (SIF), the normalized difference vegetation index (NDVI) and the near-infrared reflectance of vegetation (NIRv). The SIF, NDVI and NIRv breakpoints were detected, and the trends and change-points of phenological parameters based on these data were analyzed. The correlations between the phenological parameters and snow-related factors, precipitation, temperature, soil moisture and population density were also analyzed. The results showed that SIF and NIRv could identify breakpoints early. SIF could estimate the latest SOS and the earliest EOS. NDVI could estimate the earliest SOS and the latest EOS. The change-points of SOS_SIF were mostly concentrated from 2001 to 2003, and those of SOS_NDVI and SOS_NIRv occurred later. The change-points of EOS_SIF and EOS_NIRv were mostly concentrated from 2001 to 2007, and those of EOS_SIF occurred later. Differently from the weak correlation with SOS_SIF, SOS_NDVI and SOS_NIRv were significantly correlated with snow-related factors. The correlation between the meteorological factors in the summer and autumn and EOS_SIF was the most significant. The population density showed the highest degree of interpretation for SOS_NIRv and EOS_NDVI. The results reveal the differences and potentials of different remote-sensing parameters in estimating phenological indicators, which is helpful for better understanding the dynamic changes in phenology and the response to changes in various influencing factors. Full article

(This article belongs to the Special Issue Remote Sensing of Vegetation: Mapping, Trend Analysis, and Drivers of Change)

► Show Figures

Figure 1

21 pages, 3848 KiB

Open AccessArticle

Response of Vegetation to Different Climate Extremes on a Monthly Scale in Guangdong, China

by Leidi Wang, Fei Hu, Caiyue Zhang, Yuchen Miao, Huilin Chen, Keyou Zhong and Mingzhu Luo

Remote Sens. 2022, 14(21), 5369; https://doi.org/10.3390/rs14215369 - 26 Oct 2022

Cited by 3 | Viewed by 1393

Abstract

Climate extremes, particularly drought, often affect the ecosystem. Guangdong Province is one of the most vulnerable areas in China. Using the normalized difference vegetation index (NDVI) to capture vegetation dynamics, this study investigated vegetation responses to drought, temperature, and precipitation extremes on a monthly scale in the vegetation area of Guangdong without vegetation type changes from 1982 to 2015. As extreme temperatures rose, a drought trend occurred in most months, with a higher rate in February and April. The vegetation evenly showed a significant greening trend in all months except June and October. The vegetation activity was significantly positively correlated with the increased extreme temperatures in most months. However, it exerted a negative correlation with drought in February, April, May, June, and September, as well as precipitation extremes in February, April, and June. The response of vegetation to drought was the most sensitive in June. The vegetation tended to be more sensitive to short-term droughts (1–2 months) and had no time lag in response to drought. The results are helpful to provide references for ecological management and ecosystem protection. Full article

(This article belongs to the Special Issue Remote Sensing of Vegetation: Mapping, Trend Analysis, and Drivers of Change)

► Show Figures

Figure 1

18 pages, 7151 KiB

Open AccessArticle

Effect of Vegetation Carryover and Climate Variability on the Seasonal Growth of Vegetation in the Upper and Middle Reaches of the Yellow River Basin

by Xinru Zhang, Qian Cao, Hao Chen, Quan Quan, Changchao Li, Junyu Dong, Mengjie Chang, Shuwan Yan and Jian Liu

Remote Sens. 2022, 14(19), 5011; https://doi.org/10.3390/rs14195011 - 9 Oct 2022

Cited by 6 | Viewed by 1691

Abstract

Vegetation dynamics are often affected by climate variability, but the past state of vegetation has a non-negligible impact on current vegetation growth. However, seasonal differences in the effects of these drivers on vegetation growth remain unclear, particularly in ecologically fragile areas. We used the normalized difference vegetation index (NDVI), gross primary productivity (GPP), and leaf area index (LAI) to describe the vegetation dynamic in the upper and middle reaches of the Yellow River basin (YRB). Three active vegetation growing seasons (early, peak, and late) were defined based on phenological metrics. In light of three vegetation indicators and the climatic data, we identified the correlation between the inter-annual variation of vegetation growth in the three sub-seasons. Then, we quantified the contributions of climate variability and the vegetation growth carryover (VGC) effect on seasonal vegetation greening between 2000–2019. Results showed that both the vegetation coverage and productivity in the study area increased over a 20-year period. The VGC effect dominated vegetation growth during the three active growing seasons, and the effect increased from early to late growing season. Vegetation in drought regions was found to generally have a stronger vegetation carryover ability, implying that negative disturbances might have severer effects on vegetation in these areas. The concurrent seasonal precipitation was another positive driving factor of vegetation greening. However, sunshine duration, including its immediate and lagged impacts, had a negative effect on vegetation growth. In addition, the VGC effect can sustain into the second year. The VGC effect showed that initial ecological restoration and sustainable conservation would promote vegetation growth and increase vegetation productivity. This study provides a comprehensive perspective on understanding the climate–vegetation interactions on a seasonal scale, which helps to accurately predict future vegetation dynamics over time in ecologically fragile areas. Full article

(This article belongs to the Special Issue Remote Sensing of Vegetation: Mapping, Trend Analysis, and Drivers of Change)

► Show Figures

Figure 1

23 pages, 14459 KiB

Open AccessArticle

Linear and Non-Linear Vegetation Trend Analysis throughout Iran Using Two Decades of MODIS NDVI Imagery

by Arsalan Ghorbanian, Ali Mohammadzadeh and Sadegh Jamali

Remote Sens. 2022, 14(15), 3683; https://doi.org/10.3390/rs14153683 - 1 Aug 2022

Cited by 26 | Viewed by 3240

Abstract

Vegetation is the main component of the terrestrial Earth, and it plays an imperative role in carbon cycle regulation and surface water/energy exchange/balance. The coupled effects of climate change and anthropogenic forcing have undoubtfully impacted the vegetation cover in linear/non-linear manners. Considering the essential benefits of vegetation to the environment, it is vital to investigate the vegetation dynamics through spatially and temporally consistent workflows. In this regard, remote sensing, especially Normalized Difference Vegetation Index (NDVI), has offered a reliable data source for vegetation monitoring and trend analysis. In this paper, two decades (2000 to 2020) of Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI datasets (MOD13Q1) were used for vegetation trend analysis throughout Iran. First, the per-pixel annual NDVI dataset was prepared using the Google Earth Engine (GEE) by averaging all available NDVI values within the growing season and was then fed into the PolyTrend algorithm for linear/non-linear trend identification. In total, nearly 14 million pixels (44% of Iran) were subjected to trend analysis, and the results indicated a higher rate of greening than browning across the country. Regarding the trend types, linear was the dominant trend type with 14%, followed by concealed (11%), cubic (8%), and quadratic (2%), while 9% of the vegetation area remained stable (no trend). Both positive and negative directions were observed in all trend types, with the slope magnitudes ranging between −0.048 and 0.047 (NDVI units) per year. Later, precipitation and land cover datasets were employed to further investigate the vegetation dynamics. The correlation coefficient between precipitation and vegetation (NDVI) was 0.54 based on all corresponding observations (n = 1785). The comparison between vegetation and precipitation trends revealed matched trend directions in 60% of cases, suggesting the potential impact of precipitation dynamics on vegetation covers. Further incorporation of land cover data showed that grassland areas experienced significant dynamics with the highest proportion compared to other vegetation land cover types. Moreover, forest and cropland had the highest positive and negative trend direction proportions. Finally, independent (from trend analysis) sources were used to examine the vegetation dynamics (greening/browning) from other perspectives, confirming Iran’s greening process and agreeing with the trend analysis results. It is believed that the results could support achieving Sustainable Development Goals (SDGs) by serving as an initial stage study for establishing conservation and restoration practices. Full article

(This article belongs to the Special Issue Remote Sensing of Vegetation: Mapping, Trend Analysis, and Drivers of Change)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Remote Sensing of Vegetation: Mapping, Trend Analysis, and Drivers of Change

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (11 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI