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Seasonal Vegetation Index Changes: Cases and Solutions

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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 January 2024) | Viewed by 9149

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Special Issue Editors

Dr. Lifu Zhang

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Guest Editor

Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: hyperspectral remote sensing; temporal spectrum; precision agriculture; multidimensional analysis technology
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Dr. Yanbo Huang

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Genetics and Sustainable Agriculture Research Unit, United States Department of Agriculture, Agriculture Research Service, Starkville, MS 39762, USA
Interests: aerial application technology (manned aircraft and unmanned aerial vehicles); remote sensing for precision application (space-borne, airborne, and ground truthing); machine learning, soft computing and decision support for precision agriculture; spatial statistics for remote sensing data analysis; image processing; process modeling; optimization; control and automation
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Dr. Yi Guo

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Centre for Research in Mathematics and Data Science, Western Sydney University, Parramatta, NSW 2150, Australia
Interests: computational statistics; data science; machine learning
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Prof. Dr. Bin Wang

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Key Laboratory for Information Science of Electromagnetic Waves, Ministry of Education, Fudan University, Shanghai 200433, China
Interests: hyperspectral remote sensing; nonlinear unmixing; automatic target/object detection; intelligent information processing; machine learning

Dr. Changping Huang

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Guest Editor

Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: hyperspectral remote sensing; SIF; vegetation disease and pests; remote sensing big data and smart agriculture
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Previous research shows that the vegetation index is a simple, effective and reliable parameter to characterize the status of surface vegetation. It has achieved great application results in global and regional land-use evaluation, vegetation classification, crop yield estimation, various vegetation stresses and land productivity evaluation. The remote sensing data of various countries are developing towards high spectral resolution, high spatial resolution and high temporal resolution in recent years. The research on the time dimension is at a disadvantage in terms of depth and breadth compared with the former two, while carbon neutrality, global change, climate issues and environmental development are all closely related to time factors. It has become one of the research highlights in the field of remote sensing technology to realize the more human living space information mining by studying the change law of vegetation index with time. Therefore, the purpose of this Special Issue is to discuss the temporal application direction of vegetation index and study the positive effect of seasonal vegetation index change on economy and society.

We invite papers containing the latest research results on seasonal vegetation index changes, including the data structure designed to deal with seasonal vegetation index change, the processing method of seasonal vegetation index change data, the temporal spectrum research with the remote sensing time series change detection, the typical case study of seasonal vegetation index changes, etc. Other studies, such as the discussion on the application of various vegetation indexes, the application of seasonal vegetation index in agricultural yield estimation, the response relationship between global biomass and vegetation index, the assimilation of global change data and vegetation index data, the modeling and quantitative calculation of temporal spectrum data, are all the research directions which are appreciated.

Specifically, topics of interest for this Special Issue include (but are not limited to):

Data structure of seasonal vegetation index changes;
Data processing method of seasonal vegetation index changes;
Temporal spectrum research of vegetation;
Crop yield estimation in multiple growth periods;
Response between global change and vegetation index.

Dr. Lifu Zhang
Dr. Yanbo Huang
Dr. Yi Guo
Prof. Dr. Bin Wang
Dr. Changping Huang
Guest Editors

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Keywords

seasonal vegetation index changes
temporal spectrum
crop yield estimation
carbon neutralization
biomass change
crop stress
spectral modeling
global change detection
application case of vegetation index

Published Papers (5 papers)

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Research

20 pages, 19212 KiB

Open AccessArticle

Vegetation Greening and Its Response to a Warmer and Wetter Climate in the Yellow River Basin from 2000 to 2020

by Yan Bai, Yunqiang Zhu, Yingzhen Liu and Shu Wang

Remote Sens. 2024, 16(5), 790; https://0-doi-org.brum.beds.ac.uk/10.3390/rs16050790 - 24 Feb 2024

Cited by 1 | Viewed by 556

Abstract

Vegetation greening is time-dependent and region-specific. The uncertainty of vegetation greening under global warming has been highlighted. Thus, it is crucial to investigate vegetation greening and its response to climate change at the regional scale. The Yellow River Basin (YRB) is a vital ecological barrier in China with high ecological vulnerability and climatic sensitivity. The relationship between vegetation greening and climate change in the YRB and the relative contribution of climate change remain to be explored. Using the Enhanced Vegetation Index (EVI) and meteorological observation data, the spatiotemporal patterns of vegetation greening across the YRB in response to climate change at the basin and vegetation sub-regional scales from 2000 to 2020 were analyzed. The impact of human activities on regional greening was further quantified. Results showed that approximately 92% of the basin had experienced greening, at average annual and growing season rates of 0.0024 and 0.0034 year^–1, respectively. Greening was particularly prominent in the central and eastern YRB. Browning was more prevalent in urban areas with a high intensity of human activities, occupying less than 6.3% of the total basin, but this proportion increased significantly at seasonal scales, especially in spring. Regional greening was positively correlated with the overall warmer and wetter climate, and the partial correlation coefficients between EVI and precipitation were higher than those between EVI and temperature. However, this response varied among different seasonal scales and vegetation sub-regions. The combined effects of climate change and human activities were conducive to vegetation greening in 84.5% of the YRB during the growing season, while human activities had a stronger impact than climate change. The relative contributions of human activities to greening and browning were 65.15% and 70.30%, respectively, mainly due to the promotion of ecological rehabilitation programs and the inhibition of urbanization and construction projects. Full article

(This article belongs to the Special Issue Seasonal Vegetation Index Changes: Cases and Solutions)

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32 pages, 27758 KiB

Open AccessArticle

Vegetation Subtype Classification of Evergreen Broad-Leaved Forests in Mountainous Areas Using a Hierarchy-Based Classifier

by Shiqi Zhang, Peihao Peng, Maoyang Bai, Xiao Wang, Lifu Zhang, Jiao Hu, Meilian Wang, Xueman Wang, Juan Wang, Donghui Zhang, Xuejian Sun and Xiaoai Dai

Remote Sens. 2023, 15(12), 3053; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15123053 - 10 Jun 2023

Cited by 1 | Viewed by 1425

Abstract

Evergreen broad-leaved forests with rich biodiversity play a key role in stabilizing global vegetation productivity and maintaining land carbon sinks. However, quantitative and accurate classification results for humid, evergreen, broad-leaved forests (HEBF) and semi-humid evergreen broad-leaved forests (SEBF) with different vegetation productivity and significant differences in species composition are lacking. Remote sensing technology brings the possibility of vegetation subtype classification. Taking the mountainous evergreen broad-leaved forests distributed in Sichuan Province as an example, this study proposed a hierarchy-based classifier combined with environmental variables to quantitatively classify the two vegetation subtypes with different ecological characteristics but similar image features. Additionally, we applied Sun–Canopy–Sensor and C parameter(SCS + C) topographic correction to preprocess the images, effectively correcting the radiometric distortion and enhancing the accuracy of vegetation classification. Finally, achieving an overall accuracy (OA) of 87.91% and a Kappa coefficient of 0.76, which is higher than that of directly using the classifier to classify the two vegetation subtypes. The study revealed the widespread distribution of evergreen broad-leaved forests in Sichuan, with a clear boundary between the distribution areas of HEBF and SEBF. The HEBF in the east is located in the basin and the low marginal mountains; the SEBF is located in the southwest dry valley. The methods employed in this study offer an effective approach to vegetation classification in mountainous areas. The findings can provide guidance for ecological engineering construction, ecological protection, and agricultural and livestock development. Full article

(This article belongs to the Special Issue Seasonal Vegetation Index Changes: Cases and Solutions)

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19 pages, 7146 KiB

Open AccessArticle

Multi-Temporal Trend Analysis of Coastal Vegetation Using Metrics Derived from Hyperspectral and LiDAR Data

by Glenn M. Suir, Sam Jackson, Christina Saltus and Molly Reif

Remote Sens. 2023, 15(8), 2098; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15082098 - 16 Apr 2023

Cited by 1 | Viewed by 1681

Abstract

Monitoring and modeling of coastal vegetation and wetland systems are considered major challenges, especially when considering environmental response to hazards, disturbances, and management activities. Remote sensing applications can provide alternatives and complementary approaches to the often costly and laborious field-based collection methods traditionally used for coastal ecosystem monitoring. New and improved sensors and data analysis techniques have become available, making remote sensing applications attractive for evaluation and potential use in monitoring coastal vegetation properties and ecosystem conditions and change. This study involves the extraction of vegetation metrics from airborne LiDAR (Light Detection and Ranging) and hyperspectral imagery (HSI) to quantify coastal dune vegetation characteristics and assesses landscape-level trends from those derived metrics. HSI- and LiDAR-derived elevation (digital elevation model) and vegetation metrics (canopy height model, leaf area index, and normalized difference vegetation index) were used in conjunction with per-pixel linear regression and hot spot analyses to evaluate hurricane-induced spatial and temporal changes in elevation and vegetation properties. These assessments showed areas with greatest decreases in vegetation metric values were associated with direct tropical storm energies and processes (i.e., overwashing events eroding beach and dune features), while those with the greatest increases in vegetation metric values were in areas where overwashed sediments were distributed. This study narrows existing gaps in dune vegetation data by advancing new methodologies to classify, quantify, and estimate critical coastal vegetation metrics. The tools and methods developed in this study will ultimately improve future estimates and predictions of nearshore dynamics and impacts from disturbance events. Full article

(This article belongs to the Special Issue Seasonal Vegetation Index Changes: Cases and Solutions)

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18 pages, 4271 KiB

Open AccessArticle

Spatial and Temporal Characteristics of Water Use Efficiency in Typical Ecosystems on the Loess Plateau in the Last 20 Years, with Drivers and Implications for Ecological Restoration

by Ruixue Ma, Ximin Cui, Dacheng Wang, Shudong Wang, Hongsen Wang, Xiaojing Yao and Shenshen Li

Remote Sens. 2022, 14(22), 5632; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14225632 - 08 Nov 2022

Cited by 3 | Viewed by 1595

Abstract

The water use efficiency (WUE) is an essential indicator of carbon–water coupling between terrestrial ecosystems and the atmosphere, and it is an important parameter for studying ecosystem responses to global climate change. A comprehensive understanding of the water–carbon coupling process in the Loess Plateau can reflect the balance between the “carbon absorption” and “water consumption” in vegetation, which drives the ecosystem succession process. In recent years, scholars have gained a more comprehensive understanding of the WUE and the driving factors of the Loess Plateau. However, there is still a need to study the carbon and water coupling mechanisms of different land use types in the Loess Plateau region. In this article, based on the gross primary productivity (GPP), evapotranspiration (ET), surface cover remote sensing products, and meteorological observation data, the trend of WUE changes for different vegetation types in the Loess Plateau from 2001 to 2020 and the correlations with the Normalized Difference Vegetation Index (NDVI), precipitation, and temperature values were analyzed using the Theil–Sen median (SEN) trend analysis method and correlation coefficient analysis method. The spatial distribution patterns of the changes with the drought index showed that the multi-year average WUE value of the Loess Plateau was 1.24 g C mm⁻¹ H₂O, and the mean WUE values in different seasons were ranked as follows: summer > autumn > spring. The WUE growth rates of all vegetation types showed a decreasing trend with the increase in drought index, and the size of the WUE response rate for each vegetation type to drought was ranked as follows: grassland > forest > shrub > crop. The annual average WUE increase rate of the Loess Plateau was 0.02 g C mm⁻¹ H₂O yr⁻¹, of which 93.36% of the area showed an increasing trend. The NDVI was the dominant factor affecting the spatial and temporal variations in WUE rates in the Loess Plateau, and the correlation between the NDVI and WUE was strongest in summer. In the more arid regional ecosystems, the WUE was negatively correlated with the precipitation and temperature, but in summer the precipitation had a positive effect on the WUE. The correlation of grassland and shrub WUE rates with temperature was more sensitive to the drought index than that of the forest and crop areas, but there was also a threshold effect. Therefore, when vegetation restoration is carried out in arid and semi-arid regions, the carbon and water coupling mechanisms of different vegetation types and the reasonable allocation of regional water resources should be fully considered. Full article

(This article belongs to the Special Issue Seasonal Vegetation Index Changes: Cases and Solutions)

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32 pages, 13863 KiB

Open AccessArticle

LAI-Based Phenological Changes and Climate Sensitivity Analysis in the Three-River Headwaters Region

by Xiaoai Dai, Wenjie Fan, Yunfeng Shan, Yu Gao, Chao Liu, Ruihua Nie, Donghui Zhang, Weile Li, Lifu Zhang, Xuejian Sun, Tiegang Liu, Zhengli Yang, Xiao Fu, Lei Ma, Shuneng Liang, Youlin Wang and Heng Lu

Remote Sens. 2022, 14(15), 3748; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14153748 - 04 Aug 2022

Cited by 13 | Viewed by 2221

Abstract

Global climate changes have a great impact on terrestrial ecosystems. Vegetation is an important component of ecosystems, and the impact of climate changes on ecosystems can be determined by studying vegetation phenology. Vegetation phenology refers to the phenomenon of periodic changes in plants, such as germination, flowering and defoliation, with the seasonal change of climate during the annual growth cycle, and it is considered to be one of the most efficient indicators to monitor climate changes. This study collected the global land surface satellite leaf area index (GLASS LAI) products, meteorological data sets and other auxiliary data in the Three-River headwaters region from 2001 to 2018; rebuilt the vegetation LAI annual growth curve by using the asymmetric Gaussian (A-G) fitting method and extracted the three vegetation phenological data (including Start of Growing Season (SOS), End of Growing Season (EOS) and Length of Growing Season (LOS)) by the maximum slope method. In addition, it also integrated Sen’s trend analysis method and the Mann-Kendall test method to explore the temporal and spatial variation trends of vegetation phenology and explored the relationship between vegetation phenology and meteorological factors through a partial correlation analysis and multiple linear regression models. The results of this study showed that: (1) the SOS of vegetation in the Three-River headwaters region is concentrated between the beginning and the end of May, with an interannual change rate of −0.14 d/a. The EOS of vegetation is concentrated between the beginning and the middle of October, with an interannual change rate of 0.02 d/a. The LOS of vegetation is concentrated between 4 and 5 months, with an interannual change rate of 0.21 d/a. (2) Through the comparison and verification with the vegetation phenological data observed at the stations, it was found that the precision of the vegetation phonology extracted by the A-G method and the maximum slope method based on GLASS LAI data is higher (MAE is 7.6 d, RMSE is 8.4 d) and slightly better than the vegetation phenological data (MAE is 9.9 d, RMSE is 10.9 d) extracted based on the moderate resolution imaging spectroradiometer normalized difference vegetation index (MODIS NDVI) product. (3) The correlation between the SOS of vegetation and the average temperature in March–May is the strongest. The SOS of vegetation is advanced by 1.97 days for every 1 °C increase in the average temperature in March–May; the correlation between the EOS of vegetation and the cumulative sunshine duration in August–October is the strongest. The EOS of vegetation is advanced by 0.07 days for every 10-h increase in the cumulative sunshine duration in August–October. Full article

(This article belongs to the Special Issue Seasonal Vegetation Index Changes: Cases and Solutions)

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