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

Journal Menu

Journal Browser

Remote Sensing of Mountain and Plateau Vegetation

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Published Papers

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: 15 January 2025 | Viewed by 11075

Share This Special Issue

Special Issue Editors

Dr. Zhaoqi Wang

E-Mail Website
Guest Editor

State Key Laboratory of Plateau Ecology and Agriculture, Qinghai University, No. 251 Ningda Road, Xining 810016, China
Interests: carbon cycle model; remote sensing of vegetation; climate change; grassland ecology; vegetation productivity

Dr. Donghai Wu

E-Mail Website
Guest Editor

Department of Ecology and Evolutionary Biology, Cornell University, Ithaca, NY, USA
Interests: biogeochemical cycles; ecosystem ecology; sustainability; vegetation; remote sensing
Special Issues, Collections and Topics in MDPI journals

Dr. Hao Wang

E-Mail Website
Guest Editor

Department of Geography, School of Geography and Tourism, Shaanxi Normal University, No. 620, West Chang’an Avenue, Chang’an District, Xi’an 710119, China
Interests: remote sensing of vegetation; ecological restoration; ecosystem services; social-ecological system

Special Issue Information

Dear Colleagues,

Global warming and accelerating CO₂ concentrations have exerted widespread impacts on terrestrial ecosystems, and the effects on vegetation dynamics in mountain and plateau regions are likely to be more pronounced over the past 30 years. Mountain and plateau are typically the source of rivers, the vegetation of which plays a crucial role in climate change mitigation and local ecological security, and is essential for the sustainable development of mankind. There are increasing evidences suggested that the rate of climate change warming is accelerating in mountain and plateau environments, which will inevitably affect the changes and zonal distribution patterns of vegetation, and thus affect the regional and even global carbon cycle. However, the effects of climate change on vegetation, carbon, and water cycle in mountain and plateau regions are not yet well known. Remote sensing has been widely used for its unparalleled advantages in detecting surface information on a global or regional scale. Therefore, we welcome submissions of the researches on the application of remote sensing technology to study vegetation, water, and carbon in mountain and plateau regions and their response to climate change, etc.

Dr. Zhaoqi Wang
Dr. Donghai Wu
Dr. Hao Wang
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

remote sensing of vegetation
vegetation productivity
carbon cycle
mountain and plateau regions
alpine vegetation
remote sensing algorithm
vegetation types
elevation gradient
remote sensing in hydrology

Benefits of Publishing in a Special Issue

Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (8 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

19 pages, 3451 KiB

Open AccessArticle

High-Resolution Remotely Sensed Evidence Shows Solar Thermal Power Plant Increases Grassland Growth on the Tibetan Plateau

by Naijing Liu, Huaiwu Peng, Zhenshi Zhang, Yujin Li, Kai Zhang, Yuehan Guo, Yuzheng Cui, Yingsha Jiang, Wenxiang Gao and Donghai Wu

Remote Sens. 2024, 16(22), 4266; https://doi.org/10.3390/rs16224266 - 15 Nov 2024

Viewed by 282

Abstract

Solar energy plays a crucial role in mitigating greenhouse gas emissions in the context of global climate change. However, its deployment for green electricity generation can significantly influence regional climate and vegetation dynamics. While prior studies have examined the impacts of solar power plants on vegetation, the accuracy of these assessments has often been constrained by the availability of publicly accessible multispectral, high-resolution remotely sensed imagery. Given the abundant solar energy resources and the ecological significance of the Tibetan Plateau, a thorough evaluation of the vegetation effects associated with solar power installations is warranted. In this study, we utilize sub-meter resolution imagery from the GF-2 satellite to reconstruct the fractional vegetation cover (FVC) at the Gonghe solar thermal power plant through image classification, in situ sampling, and sliding window techniques. We then quantify the plant’s impact on FVC by comparing data from the pre-installation and post-installation periods. Our findings indicate that the Gonghe solar thermal power plant is associated with a 0.02 increase in FVC compared to a surrounding control region (p < 0.05), representing a 12.5% increase relative to the pre-installation period. Notably, the enhancement in FVC is more pronounced in the outer ring areas than near the central tower. The observed enhancement in vegetation growth at the Gonghe plant suggests potential ecological and carbon storage benefits resulting from solar power plant establishment on the Tibetan Plateau. These findings underscore the necessity of evaluating the climate and ecological impacts of renewable energy facilities during the planning and design phases to ensure a harmonious balance between clean energy development and local ecological integrity. Full article

(This article belongs to the Special Issue Remote Sensing of Mountain and Plateau Vegetation)

► Show Figures

Graphical abstract

21 pages, 5058 KiB

Open AccessArticle

Mapping Seasonal Spatiotemporal Dynamics of Alpine Grassland Forage Phosphorus Using Sentinel-2 MSI and a DRL-GP-Based Symbolic Regression Algorithm

by Jiancong Shi, Aiwu Zhang, Juan Wang, Xinwang Gao, Shaoxing Hu and Shatuo Chai

Remote Sens. 2024, 16(21), 4086; https://doi.org/10.3390/rs16214086 - 1 Nov 2024

Viewed by 624

Abstract

An accurate estimation of seasonal spatiotemporal dynamics of forage phosphorus (P) content in alpine grassland is crucial for effective grassland and livestock management. In this study, we integrated Sentinel-2 multispectral imagery (MSI) with computational hyperspectral features (CHSFs) and developed a novel symbolic regression algorithm based on deep reinforcement learning and genetic programming (DRL-GP) to estimate forage P content in alpine grasslands. Using 243 field observations collected during the regreening, grass-bearing, and yellowing periods in 2023 from the Shaliu River Basin, we generated 10 CHSF images (CHSFIs) with varying spectral dispersions (1–10 nm). Our results demonstrated the following: (1) The DRL-GP-based symbolic regression model identified the optimal CHSF and spectral dispersion for each growing season, significantly enhancing estimation accuracy. (2) Forage P content estimations using the combined CHSF and DRL-GP-based symbolic regression algorithm significantly outperformed traditional methods. Compared to original spectral features, the R² improved by 99.5%, 57.4%, and 86.2% during the regreening, grass-bearing, and yellowing periods, with corresponding MSE reductions of 84.8%, 41.5%, and 75.8% and MAE decreases of 70.7%, 57.5%, and 50.4%. Across these growing seasons, the R² increased by 322.2%, 68.2%, and 639.8% compared to MLR, 128.9%, 97.4%, and 469.2% compared to RF, and 485.1%, 65.3%, and 231.3% compared to DNN. The MSE decreased by 31%, 82.9%, and 52.4% compared to MLR, 39.9%, 42.4%, and 31.4% compared to RF, and 84.5%, 73.4%, and 81.9% compared to DNN. The MAE decreased by 32.6%, 67%, and 44.2% compared to MLR, 42.6%, 47.6%, and 37.9% compared to RF, and 60.2%, 50%, and 56.3% compared to DNN. (3) Proximity to the water system notably influenced forage P variation, with the highest increases observed within 1–2 km of water sources. These findings provide critical insights for optimizing grassland management and improving livestock productivity. Full article

(This article belongs to the Special Issue Remote Sensing of Mountain and Plateau Vegetation)

► Show Figures

Figure 1

23 pages, 37649 KiB

Open AccessArticle

Research on Forage–Livestock Balance in the Three-River-Source Region Based on Improved CASA Model

by Chenlu Hu, Yichen Tian, Kai Yin, Huiping Huang, Liping Li and Qiang Chen

Remote Sens. 2024, 16(20), 3857; https://doi.org/10.3390/rs16203857 - 17 Oct 2024

Viewed by 705

Abstract

As an important ecological barrier and a crucial base for animal husbandry in China, the forage–livestock balance in the Three-River-Source Region (TRSR) directly impacts both the degradation and recovery of grassland. This study examines the forage–livestock balance in the TRSR over the past 13 years (2010–2022) by calculating both the theoretical and actual livestock carrying capacity, thereby providing a scientific basis for regional animal husbandry policies. Firstly, the Carnegie–Ames–Stanford Approach (CASA) model was improved to fit the specific characteristics of alpine grassland ecosystem in the TRSR. This enhanced model was subsequently used to calculate the net primary productivity (NPP) of the grassland, from which the regional grassland yield and theoretical livestock carrying capacity were derived. Secondly, the actual livestock carrying capacity was calculated and spatialized based on the number of regional year-end livestock. Finally, the livestock carrying pressure index was determined using both the theoretical and actual livestock carrying capacity. The results revealed several key findings: (1) The average grassland NPP in the TRSR was 145.44 gC/m², the average grassland yield was 922.7 kg/hm², and the average theoretical livestock carrying capacity was 0.55 SU/hm² from 2010 to 2022. Notably, all three metrics showed an increasing trend over the past 13 years, which indicates the rise in grassland vegetation activities. (2) The average actual livestock carrying capacity over the 13-year period was 0.46 SU/hm², showing a decreasing trend on the whole. The spatial distribution displayed a pattern of higher capacity in the east and lower in the west. (3) Throughout the 13 years, the TRSR generally maintained a forage–livestock balance, with an average livestock carrying pressure index of 0.96 (insufficient). However, the trend of livestock carrying pressure is on the rise, with serious overloading observed in the western part of Qumalai County and the northern part of Tongde County. Slight overloading was also noted in Zhiduo, Maduo, and Zeku Counties. Notably, Tanggulashan Town, Zhiduo, Qumalai, and Maduo Counties showed significant increases in livestock carrying pressure, while Zaduo County and the eastern regions experienced significant decreases. In conclusion, this study not only provides feasible technical methods for assessing and managing the forage–livestock balance in the TRSR but also contributes significantly to the sustainable development of the region’s grassland ecosystem and animal husbandry industry. Full article

(This article belongs to the Special Issue Remote Sensing of Mountain and Plateau Vegetation)

► Show Figures

Figure 1

21 pages, 12373 KiB

Open AccessArticle

Monitoring Grassland Variation in a Typical Area of the Qinghai Lake Basin Using 30 m Annual Maximum NDVI Data

by Meng Li, Guangjun Wang, Aohan Sun, Youkun Wang, Fang Li and Sihai Liang

Remote Sens. 2024, 16(7), 1222; https://doi.org/10.3390/rs16071222 - 30 Mar 2024

Cited by 2 | Viewed by 1017

Abstract

The normalized difference vegetation index (NDVI) can depict the status of vegetation growth and coverage in grasslands, whereas coarse spatial resolution, cloud cover, and vegetation phenology limit its applicability in fine-scale research, especially in areas covering various vegetation or in fragmented landscapes. In this study, a methodology was developed for obtaining the 30 m annual maximum NDVI to overcome these shortcomings. First, the Landsat NDVI was simulated by fusing Landsat and MODIS NDVI by using the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), and then a single-peaked symmetric logistic model was employed to fit the Landsat NDVI data and derive the maximum NDVI in a year. The annual maximum NDVI was then used as a season-independent substitute to monitor grassland variation from 2001 to 2022 in a typical area covering the major vegetation types in the Qinghai Lake Basin. The major conclusions are as follows: (1) Our method for reconstructing the NDVI time series yielded higher accuracy than the existing dataset. The root mean square error (RMSE) for 91.8% of the pixels was less than 0.1. (2) The annual maximum NDVI from 2001 to 2022 exhibited spatial distribution characteristics, with higher values in the northern and southern regions and lower values in the central area. In addition, the earlier vegetation growth maximum dates were related to the vegetation type and accompanied by higher NDVI maxima in the study area. (3) The overall interannual variation showed a slight increasing trend from 2001 to 2022, and the degraded area was characterized as patches and was dominated by Alpine kobresia spp., Forb Meadow, whose change resulted from a combination of permafrost degradation, overgrazing, and rodent infestation and should be given more attention in the Qinghai Lake Basin. Full article

(This article belongs to the Special Issue Remote Sensing of Mountain and Plateau Vegetation)

► Show Figures

Figure 1

19 pages, 9980 KiB

Open AccessArticle

Spatial Heterogeneity and the Increasing Trend of Vegetation and Their Driving Mechanisms in the Mountainous Area of Haihe River Basin

by Bo Cao, Yan Wang, Xiaolong Zhang and Yan-Jun Shen

Remote Sens. 2024, 16(3), 587; https://doi.org/10.3390/rs16030587 - 4 Feb 2024

Cited by 1 | Viewed by 1435

Abstract

In addition to serving as North China’s water supply and ecological barrier, the mountainous area of the Haihe River basin (MHRB) is a crucial location for the application of ecological engineering. Vegetation is an important component in the ecological conservation and eco-hydrological progress of the MHRB. A better understanding of regional vegetation growth can be achieved by a thorough investigation of vegetation indicators. In this research, the leaf area index (LAI) and gross primary productivity (GPP) were chosen as vegetation indicators. The characteristics and driving forces of the spatiotemporal variations of LAI and GPP in the MHRB were explored through Sen’s slope, the Mann–Kendall test, the optimal parameter-based geographical detector model, and correlation analysis. From 2001 to 2018, the annual LAI and GPP increased significantly on the regional scale. The areas with significantly increased vegetation accounted for more than 81% of the MHRB. Land use was the most influential element for the spatial heterogeneity of LAI and GPP, and the humidity index was the most crucial one among climate indicators. Non-linear enhancement or bivariate enhancement was discovered between any two factors, and the strongest interaction was from land use and humidity index. The lowest vegetation cover was found in dry regions with annual precipitation below 407 mm and the humidity index under 0.41; while in both forests and large undulating mountains, higher LAI and GPP were observed. About 87% of the significantly increased vegetation was found in areas with unaltered land use. The increase in vegetation in the MHRB from 2001 to 2018 was promoted by the increased precipitation and humidity index and the reduced vapor pressure deficit. The sensitivity of GPP to climate change was stronger than that of LAI. These findings can serve as a theoretical guide for the application of ecological engineering and ecological preservation in the MHRB. Full article

(This article belongs to the Special Issue Remote Sensing of Mountain and Plateau Vegetation)

► Show Figures

Graphical abstract

16 pages, 5911 KiB

Open AccessArticle

Mountain Vegetation Classification Method Based on Multi-Channel Semantic Segmentation Model

by Baoguo Wang and Yonghui Yao

Remote Sens. 2024, 16(2), 256; https://doi.org/10.3390/rs16020256 - 9 Jan 2024

Cited by 3 | Viewed by 1703

Abstract

With the development of satellite remote sensing technology, a substantial quantity of remote sensing data can be obtained every day, but the ability to extract information from these data remains poor, especially regarding intelligent extraction models for vegetation information in mountainous areas. Because the features of remote sensing images (such as spectral, textural and geometric features) change with changes in illumination, viewing angle, scale and spectrum, it is difficult for a remote sensing intelligent interpretation model with a single data source as input to meet the requirements of engineering or large-scale vegetation information extraction and updating. The effective use multi-source, multi-resolution and multi-type data for remote sensing classification is still a challenge. The objective of this study is to develop a highly intelligent and generalizable classification model of mountain vegetation utilizing multi-source remote sensing data to achieve accurate vegetation extraction. Therefore, a multi-channel semantic segmentation model based on deep learning, FCN-ResNet, is proposed to integrate the features and textures of multi-source, multi-resolution and multi-temporal remote sensing data, thereby enhancing the differentiation of different mountain vegetation types by capturing their characteristics and dynamic changes. In addition, several sets of ablation experiments are designed to investigate the effectiveness of the model. The method is validated on Mt. Taibai (part of the Qinling-Daba Mountains), and the pixel accuracy (PA) of vegetation classification reaches 85.8%. The results show that the proposed multi-channel semantic segmentation model can effectively discriminate different vegetation types and has good intelligence and generalization ability in different mountainous areas with similar vegetation distributions. The multi-channel semantic segmentation model can be used for the rapid updating of vegetation type maps in mountainous areas. Full article

(This article belongs to the Special Issue Remote Sensing of Mountain and Plateau Vegetation)

► Show Figures

Figure 1

18 pages, 3339 KiB

Open AccessArticle

Spatial and Temporal Variation in Vegetation Cover and Its Response to Topography in the Selinco Region of the Qinghai-Tibet Plateau

by Hongxin Huang, Guilin Xi, Fangkun Ji, Yiyang Liu, Haoran Wang and Yaowen Xie

Remote Sens. 2023, 15(16), 4101; https://doi.org/10.3390/rs15164101 - 21 Aug 2023

Cited by 9 | Viewed by 1656

Abstract

In recent years, the vegetation cover in the Selinco region of the Qinghai-Tibet Plateau has undergone significant changes due to the influence of global warming and intensified human activity. Consequently, comprehending the distribution and change patterns of vegetation in this area has become a crucial scientific concern. To address this concern, the present study employed MODIS-NDVI and elevation data, integrating methodologies such as trend analysis, Hurst exponent analysis, and sequential cluster analysis to explore vegetation cover changes over the past 21 years and predict future trends, while examining their correlation with topographic factors. The study findings indicate a fluctuating upward trend in vegetation cover, with a notable decrease in 2015. Spatially, the overall fractional vegetation cover (FVC) in the study area showed a basic stability with a percentage of 78%. The analysis of future trends in vegetation cover revealed that the majority of areas (68.26%) exhibited an uncertain trend, followed by stable regions at 15.78%. The proportion of areas showing an increase and decrease in vegetation cover accounted for only 9.63% and 5.61%, respectively. Elevation and slope significantly influence vegetation cover, with a trend of decreasing vegetation cover as elevation increases, followed by an increase, and then another decrease. Likewise, as the slope increases, initially, there is a rise in vegetation cover, followed by a subsequent decline. Notably, significant abrupt changes in vegetation cover are observed within the 4800 m elevation band and the 4° slope band in the Selinco region. Moreover, aspect has no significant effect on vegetation cover. These findings offer comprehensive insights into the spatial and temporal variations of vegetation cover in the Selinco region and their association with topographic factors, thus serving as a crucial reference for future research. Full article

(This article belongs to the Special Issue Remote Sensing of Mountain and Plateau Vegetation)

► Show Figures

Figure 1

18 pages, 10335 KiB

Open AccessArticle

The Impacts of Climate and Human Activities on Grassland Productivity Variation in China

by Yayong Xue, Haibin Liang, Yuanyuan Ma, Guoxuan Xue and Jia He

Remote Sens. 2023, 15(15), 3864; https://doi.org/10.3390/rs15153864 - 3 Aug 2023

Cited by 5 | Viewed by 2216

Abstract

Grassland degradation is widespread and increasing globally, which is closely related to the sustainable development of the ecosystems and the well-being of human life in pastoral areas. Quantifying the factors influencing grassland ecosystems, specifically climate change and human activities, is of great significance for grassland restoration. However, due to the unpredictability of human activities, further research is still needed to distinguish and identify the factors affecting grasslands. In this study, we examined the changes in the gross primary productivity (GPP) of grassland cover in 10 provinces (autonomous regions) of China from 2000 to 2018 and selected three representative climate factors (temperature, precipitation, solar radiation) and six factors covering socioeconomic (primary industry production and population), animal husbandry (large livestock and sheep populations), and national policies (grazing areas, rodent, and pest control) to characterize human activities; then, we quantified the effects and contribution of climate and human factors using three analysis methods (partial correlation analysis, geographical and temporal weighted regression model, and Lindeman Merenda Gold method). The results indicated that the GPP of grassland presented an obvious uptrend (4.75 g C m⁻² yr⁻¹, p < 0.05). Among the nine factors, sheep, precipitation, and temperature were the primary factors affecting grassland dynamics. Additionally, the GPP dynamics of grassland were mainly dominated by human activities in seven provinces (autonomous regions). These findings provide decision support for protecting grassland ecosystems and implementing ecological restoration policies in China. Full article

(This article belongs to the Special Issue Remote Sensing of Mountain and Plateau Vegetation)

► Show Figures

Journal Menu

Journal Browser

Remote Sensing of Mountain and Plateau Vegetation

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (8 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI