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Remote Sensing
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Applications of Remote Sensing in Vegetation Cover and Phenology Observation
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Applications of Remote Sensing in Vegetation Cover and Phenology Observation

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

Deadline for manuscript submissions: 15 July 2025 | Viewed by 2634

Special Issue Editors

Dr. Raffaella Matarrese

E-Mail Website
Guest Editor
CNR—IRSA, 70125 Bari, Italy
Interests: aerial and satellite imagery applied to land monitoring; hydrological modelling; environmental monitoring
Dr. Annarita D’Addabbo

E-Mail Website
Guest Editor
IREA, National Research Council, Bari, Italy
Interests: artificial intelligence; machine learning; pattern recognition
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote sensing, i.e., satellite, aerial, and UAV imagery, has revolutionized environmental monitoring by enabling large-scale monitoring of vegetation cover and phenology. Remote sensing technologies, such as multispectral and hyperspectral imaging, LiDAR, and thermal sensors, provide reliable data for tracking changes in vegetation cover and phenological cycles over time. This field of study is critical as it supports sustainable land management, agricultural productivity, and biodiversity conservation, thereby addressing key environmental and socioeconomic challenges.

The Special Issue aims to consolidate recent advances and techniques in the application of remote sensing to vegetation cover and phenology monitoring. Recent attempts to bridge the gap between technological innovation and practical applications have been robust, promoting research that provides accurate, efficient remote sensing data that can be applied to ecological monitoring.

Submissions are invited for a wide range of topics, including the following:

  • Advances in remote sensing technology for monitoring plant phenology.
  • Case studies illustrating the application of remote sensing in biodiversity.
  • Algorithms and models for processing and interpreting remote sensing data.
  • Long-term vegetative and phenological changes in response to climate change and variability.
  • The combination of remote sensing data with ground-based observations and other geographic data.
  • Applications in agricultural, forestry and urban planning.
  • Classification techniques to improve land cover and land use accuracy.

This Special Issue welcomes a variety of articles, including original research articles, research papers, technical articles and case studies. These contributions should provide new insights, propose new methods or provide comprehensive analyses that contribute to remote sensing in plant monitoring.

Dr. Raffaella Matarrese
Dr. Annarita D’Addabbo
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 monitoring
  • plant phenology
  • multispectral and hyperspectral imaging
  • environmental monitoring
  • satellite, aerial and UAV data fusion
  • vegetation pathology

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Published Papers (2 papers)

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Research

23 pages, 3871 KiB  
Article
Automating the Derivation of Sugarcane Growth Stages from Earth Observation Time Series
by Neha Joshi, Daniel M. Simms and Paul J. Burgess
Remote Sens. 2024, 16(22), 4244; https://doi.org/10.3390/rs16224244 - 14 Nov 2024
Viewed by 1195
Abstract
Sugarcane is a high-impact crop used in the majority of global sugar production, with India being the second largest global producer. Understanding the timing and length of sugarcane growth stages is critical to improving the sustainability of sugarcane management. Earth observation (EO) data [...] Read more.
Sugarcane is a high-impact crop used in the majority of global sugar production, with India being the second largest global producer. Understanding the timing and length of sugarcane growth stages is critical to improving the sustainability of sugarcane management. Earth observation (EO) data have been shown to be sensitive to the variation in sugarcane growth, but questions remain as to how to reliably extract sugarcane phenology over wide areas so that this information can be used for effective management. This study develops an automated approach to derive sugarcane growth stages using EO data from Landsat-8 and Sentinel-2 satellite data in the Indian state of Andhra Pradesh. The developed method is then evaluated in the State of Telangana. Normalised difference vegetation index (NDVI) EO data from Landsat-8 and Sentinel-2 were pre-processed to filter out clouds and to harmonise sensor response. Pixel-based cloud filtering was selected over filtering by scene in order to increase the temporal frequency of observations. Harmonising data from two different sensors further increased temporal resolution to 3–6 days (70% of sampled fields). To automate seasonal decomposition, harmonised signals were resampled at 14 days, and low-frequency components, related to seasonal growth, were extracted using a fast Fourier transform. The start and end of each season were extracted from the time series using difference of Gaussian and were compared to assessments based on visual observation for both Unit 1 (R2 = 0.72–0.84) and Unit 2 (R2 = 0.78–0.82). A trapezoidal growth model was then used to derive crop growth stages from satellite-measured phenology for better crop management information. Automated assessments of the start and the end of mid-season growth stages were compared to visual observations in Unit 1 (R2 = 0.56–0.72) and Unit 2 (R2 = 0.36–0.79). Outliers were found to result from cloud cover that was not removed by the initial screening as well as multiple crops or harvesting dates within a single field. These results demonstrate that EO time series can be used to automatically determine the growth stages of sugarcane in India over large areas, without the need for prior knowledge of planting and harvest dates, as a tool for improving sustainable production. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Vegetation Cover and Phenology Observation)
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16 pages, 7535 KiB  
Article
Satellite Observations Reveal Northward Vegetation Greenness Shifts in the Greater Mekong Subregion over the Past 23 Years
by Bowen Deng, Chenli Liu, Enwei Zhang, Mengjiao He, Yawen Li and Xingwu Duan
Remote Sens. 2024, 16(17), 3302; https://doi.org/10.3390/rs16173302 - 5 Sep 2024
Cited by 2 | Viewed by 960
Abstract
The Greater Mekong Subregion (GMS) economic cooperation program is an effective and fruitful regional cooperation initiative for socioeconomic development in Asia; however, the vegetation change trends and directions in the GMS caused by rapid development remain unknown. In particular, there is a current [...] Read more.
The Greater Mekong Subregion (GMS) economic cooperation program is an effective and fruitful regional cooperation initiative for socioeconomic development in Asia; however, the vegetation change trends and directions in the GMS caused by rapid development remain unknown. In particular, there is a current lack of comparative studies on vegetation changes in various countries in the GMS. Based on the MODIS normalized difference vegetation index (NDVI) time series data, this study analyzed the spatiotemporal patterns of vegetation coverage and their trends in the GMS from 2000 to 2022 using the Theil–Sen slope estimation, the Mann–Kendall mutation test, and the gravity center migration model. The key findings were as follows: (1) the NDVI in the GMS showed an overall upward fluctuating trend over the past 23 years, with an annual growth rate of 0.11%. The NDVI changes varied slightly between seasons, with the greatest increases recorded in summer and winter. (2) The spatial distribution of NDVI in the GMS varied greatly, with higher NDVI values in the north–central region and lower NDVI values in the south. (3) A total of 66.03% of the GMS area showed increments in vegetation during the studied period, mainly in south–central Myanmar, northeastern Thailand, Vietnam, and China. (4) From 2000 to 2022, the gravity center of vegetation greenness shifted northward in the GMS, especially from 2000 to 2005, indicating that the growth rates of vegetation in the north–central part of the GMS were higher than those in the south. Furthermore, the vegetation coverage in all countries, except Cambodia, increased, with the most pronounced growth recorded in China. Overall, these findings can provide scientific evidence for the GMS to enhance ecological protection and sustainable development. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Vegetation Cover and Phenology Observation)
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