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Remote Sensing of Mountain and Plateau Vegetation

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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: 30 June 2024 | Viewed by 5111

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Special Issue Editors

Dr. Zhaoqi Wang

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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

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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

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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

Published Papers (5 papers)

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Research

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

Viewed by 454

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)

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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 - 04 Feb 2024

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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)

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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 - 09 Jan 2024

Cited by 1 | Viewed by 932

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)

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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 2 | Viewed by 999

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)

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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 - 03 Aug 2023

Cited by 2 | Viewed by 1234

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)

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