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Vegetation Dynamics Revealed by Remote Sensing and Its Feedback to Regional and Global Climate

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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 (31 May 2022) | Viewed by 26572

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Special Issue Editors

Dr. Xuejia Wang

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Guest Editor

College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China
Interests: vegetation change; land–atmosphere interactions; model simulation; climate change
Special Issues, Collections and Topics in MDPI journals

Dr. Tinghai Ou

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Guest Editor

Department of Earth Science, University of Gothernburg, 405 30 Gothenburg, Sweden
Interests: climate extreme events; land–atmosphere interaction; climate modeling
Special Issues, Collections and Topics in MDPI journals

Dr. Wenxin Zhang

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Guest Editor

Department of Physical Geography and Ecosystem Science, Lund University, 221 00 Lund, Sweden
Interests: soil thermal regime; vegetation dynamics; biophysical properties of vegetaton; land surface models
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Youhua Ran

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Guest Editor

Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
Interests: remote sensing and modeling of the frozen ground and environment; climate change
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Vegetation, as one of the crucial underlying land surfaces, plays an important role in terrestrial ecosystems and the Earth's climate system. Under the impact of climate warming, vegetation exhibits clear diverse responses, such as greening and browning, which have been reported by many remote sensing studies. Vegetation is an important and sensitive indicator of climate and environment evolutions, underscoring the need to better detect and understand vegetation physiological and phenological responses, analyze mechanims of how changes in land surface properties (e.g. surface albedo and roughness length) are associated with vegetation dynamics, and identify climate and ecological feedbacks of vegetation changes. The recent development of satellite remote sensing and its derived products provide great opportunities to study vegetation dynamics and its feedback to regional and global climate system. Moreover, some of the new generation of climate models, such as CMIP6 Earth system models, which include dynamic vegetation, are state-of-the-art tools for investigating the feedback of vegetation changes.

For this Special Issue, contributions are sought which demonstrate the application of a variety of high-resolution satellite data, global and regional numerical models, and machine learning methods to obtain the fine classification of vegetation, detect vegetation dynamic changes, and examine interactions between vegetation and climate/ecological systems, especially for high-latitude and high-altitude regions. We would like to invite you to contribute to the Special Issue. Submissions are encouraged to cover a wide range of topics, which may include, but are not limited to, the following:

Vegetation mapping;
Vegetation changes from various remote sensing data sources;
Response of vegetation to climate change;
Feedback of vegetation change to climate;
Dynamic vegetation modeling;
Ecological effect of vegetation change.

Dr. Xuejia Wang
Dr. Tinghai Ou
Dr. Wenxin Zhang
Dr. Youhua Ran
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 type
vegetation phenology
change detection
model simulation
response to climate change
feedback to climate change

Published Papers (11 papers)

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Editorial

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4 pages, 200 KiB

Open AccessEditorial

An Overview of Vegetation Dynamics Revealed by Remote Sensing and Its Feedback to Regional and Global Climate

by Xuejia Wang, Tinghai Ou, Wenxin Zhang and Youhua Ran

Remote Sens. 2022, 14(20), 5275; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14205275 - 21 Oct 2022

Cited by 1 | Viewed by 1501

Abstract

Vegetation, as one of the crucial underlying land surfaces, plays an important role in terrestrial ecosystems and the Earth’s climate system through the alternation of its phenology, type, structure, and function [...] Full article

(This article belongs to the Special Issue Vegetation Dynamics Revealed by Remote Sensing and Its Feedback to Regional and Global Climate)

Research

Jump to: Editorial

20 pages, 6961 KiB

Open AccessArticle

The Response of Vegetation to Regional Climate Change on the Tibetan Plateau Based on Remote Sensing Products and the Dynamic Global Vegetation Model

by Mingshan Deng, Xianhong Meng, Yaqiong Lu, Zhaoguo Li, Lin Zhao, Hanlin Niu, Hao Chen, Lunyu Shang, Shaoying Wang and Danrui Sheng

Remote Sens. 2022, 14(14), 3337; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14143337 - 11 Jul 2022

Cited by 7 | Viewed by 1911

Abstract

Changes in vegetation dynamics play a critical role in terrestrial ecosystems and environments. Remote sensing products and dynamic global vegetation models (DGVMs) are useful for studying vegetation dynamics. In this study, we revised the Community Land Surface Biogeochemical Dynamic Vegetation Model (referred to as the BGCDV_CTL experiment) and validated it for the Tibetan Plateau (TP) by comparing vegetation distribution and carbon flux simulations against observations. Then, seasonal–deciduous phenology parameterization was adopted according to the observed parameters (referred to as the BGCDV_NEW experiment). Compared to the observed parameters, monthly variations in gross primary productivity (GPP) showed that the BGCDV_NEW experiment had the best performance against the in situ observations on the TP. The climatology from the remote sensing and simulated GPPs showed similar patterns, with GPP increasing from northwest to southeast, although the BGCDV_NEW experiment overestimated GPP in the semi-arid and arid regions of the TP. The results show that temperature warming was the dominant factor resulting in the increase in GPP based on the remote sensing products, while precipitation enhancement was the reason for the GPP increase in the model simulation. Full article

(This article belongs to the Special Issue Vegetation Dynamics Revealed by Remote Sensing and Its Feedback to Regional and Global Climate)

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23 pages, 4766 KiB

Open AccessArticle

Quantitative Analysis of Natural and Anthropogenic Factors Influencing Vegetation NDVI Changes in Temperate Drylands from a Spatial Stratified Heterogeneity Perspective: A Case Study of Inner Mongolia Grasslands, China

by Shengkun Li, Xiaobing Li, Jirui Gong, Dongliang Dang, Huashun Dou and Xin Lyu

Remote Sens. 2022, 14(14), 3320; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14143320 - 10 Jul 2022

Cited by 18 | Viewed by 2573

Abstract

The detection and attribution of vegetation dynamics in drylands is an important step for the development of effective adaptation and mitigation strategies to combat the challenges posed by human activities and climate change. However, due to the spatial heterogeneity and interactive influences of various factors, quantifying the contributions of driving forces on vegetation change remains challenging. In this study, using the normalized difference vegetation index (NDVI) as a proxy of vegetation growth status and coverage, we analyzed the temporal and spatial characteristics of the NDVI in China’s Inner Mongolian grasslands using Theil–Sen slope statistics and Mann–Kendall trend test methods. In addition, using the GeoDetector method, a spatially-based statistical technique, we assessed the individual and interactive influences of natural factors and human activities on vegetation-NDVI change. The results show that the growing season average NDVI exhibited a fluctuating upward trend of 0.003 per year from 2000 to 2018. The areas with significant increases in NDVI (p < 0.05) accounted for 45.63% of the entire region, and they were mainly distributed in the eastern part of the Mu Us sandy land and the eastern areas of the Greater Khingan Range. The regions with a decline in the NDVI were mainly distributed in the central and western regions of the study area. The GeoDetector results revealed that both natural and human factors had significant impacts on changes in the NDVI (p < 0.001). Precipitation, livestock density, wind speed, and population density were the dominant factors affecting NDVI changes in the Inner Mongolian grasslands, explaining more than 15% of the variability, while the contributions of the two topography factors (terrain slope and slope aspect) were relatively low (less than 2%). Furthermore, NDVI changes responded to the changes in the level of specific influencing factors in a nonlinear way, and the interaction of two factors enhanced the effect of each singular factor. The interaction between precipitation and temperature was the highest among all factors, accounting for 39.3% of NDVI variations. Findings from our study may aid policymakers in better understanding the relative importance of various factors and the impacts of the interactions between factors on vegetation change, which has important implications for preventing and mitigating land degradation and achieving sustainable pasture use in dryland ecosystems. Full article

(This article belongs to the Special Issue Vegetation Dynamics Revealed by Remote Sensing and Its Feedback to Regional and Global Climate)

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17 pages, 7725 KiB

Open AccessArticle

Heatwaves Significantly Slow the Vegetation Growth Rate on the Tibetan Plateau

by Caixia Dong, Xufeng Wang, Youhua Ran and Zain Nawaz

Remote Sens. 2022, 14(10), 2402; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14102402 - 17 May 2022

Cited by 9 | Viewed by 2021

Abstract

In recent years, heatwaves have been reported frequently by literature and the media on the Tibetan Plateau. However, it is unclear how alpine vegetation responds to the heatwaves on the Tibetan Plateau. This study aimed to identify the heatwaves using long-term meteorological data and examine the impact of heatwaves on vegetation growth rate with remote sensing data. The results indicated that heatwaves frequently occur in June, July, and August on the Tibetan Plateau. The average frequency of heatwaves had no statistically significant trends from 2000 to 2020 for the entire Tibetan Plateau. On a monthly scale, the average frequency of heatwaves increased significantly (p < 0.1) in August, while no significant trends were in June and July. The intensity of heatwaves indicated a negative correlation with the vegetation growth rate anomaly (ΔVGR) calculated from the normalized difference vegetation index (NDVI) (r = −0.74, p < 0.05) and the enhanced vegetation index (EVI) (r = −0.61, p < 0.1) on the Tibetan Plateau, respectively. Both NDVI and EVI consistently demonstrate that the heatwaves slow the vegetation growth rate. This study outlines the importance of heatwaves to vegetation growth to enrich our understanding of alpine vegetation response to increasing extreme weather events under the background of climate change. Full article

(This article belongs to the Special Issue Vegetation Dynamics Revealed by Remote Sensing and Its Feedback to Regional and Global Climate)

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27 pages, 5478 KiB

Open AccessArticle

Changes in Vegetation Dynamics and Relations with Extreme Climate on Multiple Time Scales in Guangxi, China

by Leidi Wang, Fei Hu, Yuchen Miao, Caiyue Zhang, Lei Zhang and Mingzhu Luo

Remote Sens. 2022, 14(9), 2013; https://doi.org/10.3390/rs14092013 - 22 Apr 2022

Cited by 12 | Viewed by 1723

Abstract

Understanding the responses of vegetation to climate extremes is important for revealing vegetation growth and guiding environmental management. Guangxi was selected as a case region in this study. This study investigated the spatial-temporal variations of the Normalized Difference Vegetation Index (NDVI), and quantitatively explored effects of climate extremes on vegetation on multiple time scales during 1982–2015 by applying the Pearson correlation and time-lag analyses. The annual NDVI significantly increased in most areas with a regional average rate of 0.00144 year⁻¹, and the highest greening rate appeared in spring. On an annual scale, the strengthened vegetation activity was positively correlated with the increased temperature indices, whereas on a seasonal or monthly scale, this was the case only in spring and summer. The influence of precipitation extremes mainly occurred on a monthly scale. The vegetation was negatively correlated with both the decreased precipitation in February and the increased precipitation in summer months. Generally, the vegetation significantly responded to temperature extremes with a time lag of at least one month, whereas it responded to precipitation extremes with a time lag of two months. This study highlights the importance of accounting for vegetation-climate interactions. Full article

(This article belongs to the Special Issue Vegetation Dynamics Revealed by Remote Sensing and Its Feedback to Regional and Global Climate)

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18 pages, 1677 KiB

Open AccessArticle

Direct and Legacy Effects of Spring Temperature Anomalies on Seasonal Productivity in Northern Ecosystems

by Hanna Marsh and Wenxin Zhang

Remote Sens. 2022, 14(9), 2007; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14092007 - 21 Apr 2022

Cited by 7 | Viewed by 2188

Abstract

Warmer or cooler spring in northern high latitudes will, for the most part, directly impact gross primary productivity (GPP) of ecosystems, but also carry consequences for the upcoming seasonal GPP. Spatiotemporal patterns of these legacy effects are still largely unknown but important for improving our understanding of how plant phenology is associated with vegetation dynamics. In this study, impacts of spring temperature anomalies on spring, summer and autumn GPP were investigated, and the dominant drivers of summer and autumn GPP including air temperature, vapor pressure deficit and soil moisture have been explored for northern ecosystems (>30°N). Three remote sensing products of seasonal GPP (GOSIF-GPP, NIRv-GPP and FluxSat-GPP) over 2001–2018, all based on a spatial resolution of 0.05°, were employed. Our results indicate that legacy effects from spring temperature are most pronounced in summer, where they have stimulating effects on the Arctic ecosystem productivity. Spring warming likely lessens the harsh climatic constraints that govern the Arctic tundra and extends the growing season length. Further south, legacy effects are mainly negative. This strengthens the hypothesis that enhanced vegetation growth in spring will increase plant water demand and stress in summer and autumn. Soil moisture is the dominant control of summer GPP in temperate regions. However, the dominant meteorological variables controlling vegetation growth may differ depending on the GPP products, highlighting the need to address uncertainties among different methods of estimating GPP. Full article

(This article belongs to the Special Issue Vegetation Dynamics Revealed by Remote Sensing and Its Feedback to Regional and Global Climate)

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17 pages, 13392 KiB

Open AccessArticle

Spatiotemporal Dynamics of Land Surface Albedo and Its Influencing Factors in the Qilian Mountains, Northeastern Tibetan Plateau

by Jichun Li, Guojin Pang, Xuejia Wang, Fei Liu and Yuting Zhang

Remote Sens. 2022, 14(8), 1922; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14081922 - 15 Apr 2022

Cited by 8 | Viewed by 1876

Abstract

Land surface albedo directly determines the distribution of radiant energy between the surface and the atmosphere, and it is a key parameter affecting the energy balance on the land surface. However, the spatiotemporal dynamics of land surface albedo and associated influencing factors in the Qilian Mountains (QM) have been rarely reported. By using the long-time series data products of MODIS shortwave albedo, normalized difference vegetation index (NDVI), and snow cover with a spatial resolution of 0.05° from 2001 to 2020, this paper analyzes the temporal and spatial variations of land surface albedo in the QM over the past 20 years and its influencing factors. The analysis results show that the multi-year average surface albedo in the QM has obvious differences in spatial distribution: it increases with the altitude, and it is high in the west (at the west of 98° E) and low in the east. Meanwhile, the surface albedo has different distribution characteristics in different seasons: the spatial distribution of surface albedo is similar in spring and autumn; the areas with a high surface albedo in summer are significantly fewer than those in other seasons. Besides, in the past 20 years, the annual average surface albedo has shown a weak growth trend in the QM, with a change rate of 5 × 10⁻³/10a, and the minimum and maximum values were reached in 2001 and 2019, respectively. In addition, the annual variation of the surface albedo in the QM showed a “U” shape, with the largest variation in January and the smallest variation in August. The annual variation of surface albedo is significantly positively correlated with snow cover (r = 0.96) and significantly negatively correlated with NDVI (r = −0.91). Moreover, the interannual variation of the surface albedo in the QM is closely related to land surface cover and is greatly affected by snow cover. Spatially, the annual variation of surface albedo in most areas of the QM is dominated by the change of snow cover, and the increase of surface albedo in the middle area is consistent with the increase of snow cover, while the decrease of albedo in the edge area is related to the improvement of vegetation cover. The results of this study provide a scientific basis for studying the climate and environmental changes caused by changes in the surface of the QM and making ecological environment restoration strategies. Full article

(This article belongs to the Special Issue Vegetation Dynamics Revealed by Remote Sensing and Its Feedback to Regional and Global Climate)

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19 pages, 9901 KiB

Open AccessArticle

The Forest Change Footprint of the Upper Indus Valley, from 1990 to 2020

by Xinrong Yan and Juanle Wang

Remote Sens. 2022, 14(3), 744; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14030744 - 05 Feb 2022

Cited by 3 | Viewed by 1913

Abstract

The upper Indus Valley is the most important and vulnerable water tower in the South Asian subcontinent, which provides a vital water supply for 230 million people in the basin. Forests play an important role in water conservation in this region, and the security of upstream forests forms the foundation downstream water and food security. However, a big challenge is to effectively monitor the dynamics of the forest in this region. Thus, we used the LandTrendr spectral-temporal segmentation algorithm combined with 8203 scenes of multi-source remote sensing data to study the forest change footprint in the upper Indus Valley. The overall accuracy of LandTrendr extraction for forest disturbance and recovery was 86.01%, and the Kappa coefficient was 0.73. The results showed the following: (1) From 1990 to 2020, the area of forest recovery was 1.01% more than that of disturbance, 70% of disturbance occurred between 1990 and 2001, and 60% of recovery occurred between 1999 and 2012. (2) Although the overall trend of forest disturbance and recovery was balanced, there were significant differences in forest management status among the different regions. Nepal has the highest forest stability, India has the largest area of forest disturbance, and Pakistan and China have the largest areas of forest recovery. (3) India’s Himachal Pradesh and Jammu and Kashmir are the two provinces with the largest disturbed areas, primarily due to grazing, fires, and commercial tree planting. Pakistan’s North-West Frontier, Azad Kashmir, and China’s Tibet Ali region were major contributors to the recovery, which was driven by afforestation policies in both countries. This study provides an important data base and monitoring method for planning land and forest use in Indus Valley countries, protecting fragile environments, and promoting policies for the Sustainable Development Goals. Full article

(This article belongs to the Special Issue Vegetation Dynamics Revealed by Remote Sensing and Its Feedback to Regional and Global Climate)

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17 pages, 8625 KiB

Open AccessArticle

Effects of Environmental Factors on the Changes in MODIS NPP along DEM in Global Terrestrial Ecosystems over the Last Two Decades

by Zhaoqi Wang, Hong Wang, Tongfang Wang, Lina Wang, Xiaotao Huang, Kai Zheng and Xiang Liu

Remote Sens. 2022, 14(3), 713; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14030713 - 02 Feb 2022

Cited by 12 | Viewed by 2071

Abstract

Global warming has exerted widespread impacts on the terrestrial ecosystem in the past three decades. Vegetation is an important part of the terrestrial ecosystem, and its net primary productivity (NPP) is an important variable in the exchange of materials and energy in the terrestrial ecosystem. However, the effect of climate variation on the spatial pattern of zonal distribution of NPP has remained unclear over the past two decades. Therefore, we analyzed the spatiotemporal patterns and trends of MODIS NPP and environmental factors (temperature, radiation, and soil moisture) derived from three sets of reanalysis data. The moving window method and digital elevation model (DEM) were used to explore their changes along elevation gradients. Finally, we explored the effect of environmental factors on the changes in NPP and its elevation distribution patterns. Results showed that nearly 60% of the global area exhibited an increase in NPP with increasing elevation. Soil moisture has the largest uncertainty either in the spatial pattern or inter-annual variation, while temperature has the smallest uncertainty among the three environmental factors. The uncertainty of environmental factors is also reflected in its impact on the elevation distribution of NPP, and temperature is still the main dominating environmental factor. Our research results imply that the carbon sequestration capability of vegetation is becoming increasingly prominent in high-elevation regions. However, the quantitative evaluation of its carbon sink (source) functions needs further research under global warming. Full article

(This article belongs to the Special Issue Vegetation Dynamics Revealed by Remote Sensing and Its Feedback to Regional and Global Climate)

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19 pages, 2773 KiB

Open AccessArticle

Assessing the Effects of Time Interpolation of NDVI Composites on Phenology Trend Estimation

by Xueying Li, Wenquan Zhu, Zhiying Xie, Pei Zhan, Xin Huang, Lixin Sun and Zheng Duan

Remote Sens. 2021, 13(24), 5018; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13245018 - 10 Dec 2021

Cited by 10 | Viewed by 3234

Abstract

The accurate evaluation of shifts in vegetation phenology is essential for understanding of vegetation responses to climate change. Remote-sensing vegetation index (VI) products with multi-day scales have been widely used for phenology trend estimation. VI composites should be interpolated into a daily scale for extracting phenological metrics, which may not fully capture daily vegetation growth, and how this process affects phenology trend estimation remains unclear. In this study, we chose 120 sites over four vegetation types in the mid-high latitudes of the northern hemisphere, and then a Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43A4 daily surface reflectance data was used to generate a daily normalized difference vegetation index (NDVI) dataset in addition to an 8-day and a 16-day NDVI composite datasets from 2001 to 2019. Five different time interpolation methods (piecewise logistic function, asymmetric Gaussian function, polynomial curve function, linear interpolation, and spline interpolation) and three phenology extraction methods were applied to extract data from the start of the growing season and the end of the growing season. We compared the trends estimated from daily NDVI data with those from NDVI composites among (1) different interpolation methods; (2) different vegetation types; and (3) different combinations of time interpolation methods and phenology extraction methods. We also analyzed the differences between the trends estimated from the 8-day and 16-day composite datasets. Our results indicated that none of the interpolation methods had significant effects on trend estimation over all sites, but the discrepancies caused by time interpolation could not be ignored. Among vegetation types with apparent seasonal changes such as deciduous broadleaf forest, time interpolation had significant effects on phenology trend estimation but almost had no significant effects among vegetation types with weak seasonal changes such as evergreen needleleaf forests. In addition, trends that were estimated based on the same interpolation method but different extraction methods were not consistent in showing significant (insignificant) differences, implying that the selection of extraction methods also affected trend estimation. Compared with other vegetation types, there were generally fewer discrepancies between trends estimated from the 8-day and 16-day dataset in evergreen needleleaf forest and open shrubland, which indicated that the dataset with a lower temporal resolution (16-day) can be applied. These findings could be conducive for analyzing the uncertainties of monitoring vegetation phenology changes. Full article

(This article belongs to the Special Issue Vegetation Dynamics Revealed by Remote Sensing and Its Feedback to Regional and Global Climate)

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30 pages, 18203 KiB

Open AccessArticle

Spatial–Temporal Evolution of Vegetation NDVI in Association with Climatic, Environmental and Anthropogenic Factors in the Loess Plateau, China during 2000–2015: Quantitative Analysis Based on Geographical Detector Model

by Yi Dong, Dongqin Yin, Xiang Li, Jianxi Huang, Wei Su, Xuecao Li and Hongshuo Wang

Remote Sens. 2021, 13(21), 4380; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13214380 - 30 Oct 2021

Cited by 33 | Viewed by 3626

Abstract

In the Loess Plateau (LP) of China, the vegetation degradation and soil erosion problems have been shown to be curbed after the implementation of the Grain for Green program. In this study, the LP is divided into the northwestern semi-arid area and the southeastern semi-humid area using the 400 mm isohyet. The spatial–temporal evolution of the vegetation NDVI during 2000–2015 are analyzed, and the driving forces (including factors of climate, environment, and human activities) of the evolution are quantitatively identified using the geographical detector model (GDM). The results showed that the annual mean NDVI in the entire LP was 0.529, and it decreased from the semi-humid area (0.619) to the semi-arid area (0.346). The mean value of the coefficient of variation of the NDVI was 0.1406, and it increased from the semi-humid area (0.1165) to the semi-arid area (0.1926). The annual NDVI growth rate in the entire LP was 0.0079, with the NDVI growing faster in the semi-humid area (0.0093) than in the semi-arid area (0.0049). The largest increments of the NDVI were from grassland, farmland, and woodland. The GDM results revealed that changes in the spatial distribution of the NDVI could be primarily explained by the climatic and environmental factors in the semi-arid area, such as precipitation, soil type, and vegetation type, while the changes were mainly explained by the anthropogenic factors in the semi-humid area, such as the GDP density, land-use type, and population density. The interactive analysis showed that interactions between factors strengthened the impacts on the vegetation change compared with an individual factor. Furthermore, the ranges/types of factors suitable for vegetation growth were determined. The conclusions of this study have important implications for the formulation and implementation of ecological conservation and restoration strategies in different regions of the LP. Full article

(This article belongs to the Special Issue Vegetation Dynamics Revealed by Remote Sensing and Its Feedback to Regional and Global Climate)

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