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Remote Sensing Application for Monitoring Grassland
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Remote Sensing Application for Monitoring Grassland

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: closed (31 August 2021) | Viewed by 14267

Special Issue Editors

Prof. Dr. Xulin Guo

E-Mail Website
Guest Editor
Department of Geography and Planning, University of Saskatchewan, Kirk Hall 117 Science Place, Saskatoon, SK S7N 5C8, Canada
Interests: remote sensing; grassland; modeling; ecology; conservation; disturbance; ecosystem function and services
Dr. Anne Smith

E-Mail Website
Guest Editor
Research scientist at Agriculture and Agri-Food Canada, AAFC, Lethbridge Research and Development Centre; Adjunct Professor at University of Lethbridge
Interests: grassland; ecology; ecosystem; remote sensing; management; productivity; invasive species detection

Special Issue Information

Dear Colleagues,

Worldwide, the sustainability of grassland ecosystems is under threat due to human disturbance and climate change. Grassland ecosystems provide a variety of goods and services, yet information on the status of these ecosystems is scant. Remote sensing provides a suitable tool for monitoring grassland biophysical characteristics at multi-spatial and multi-temporal scales.

Researchers are encouraged to contribute to a Special Issue of Sensors entitled “Remote Sensing Application for Monitoring Grassland”. This Special Issue will offer a collection of papers on remote sensing applications in a variety of grassland ecosystems under different management systems, and highlight improvements in approaches to the use of multi-spatial, multi-spectral, and multi-temporal remote sensing for grassland ecosystem monitoring.

Prof. Dr. Xulin Guo
Dr. Anne Smith
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. Sensors 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 2600 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

  • grassland types
  • sensor approaches
  • disturbance
  • management
  • long-term dynamics
  • human and climate interactions

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Published Papers (4 papers)

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17 pages, 8928 KiB  
Article
A Semi-Automated Method to Extract Green and Non-Photosynthetic Vegetation Cover from RGB Images in Mixed Grasslands
by Dandan Xu, Yihan Pu and Xulin Guo
Sensors 2020, 20(23), 6870; https://doi.org/10.3390/s20236870 - 1 Dec 2020
Cited by 8 | Viewed by 2948
Abstract
Green (GV) and non-photosynthetic vegetation (NPV) cover are both important biophysical parameters for grassland research. The current methodology for cover estimation, including subjective visual estimation and digital image analysis, requires human intervention, lacks automation, batch processing capabilities and extraction accuracy. Therefore, this study [...] Read more.
Green (GV) and non-photosynthetic vegetation (NPV) cover are both important biophysical parameters for grassland research. The current methodology for cover estimation, including subjective visual estimation and digital image analysis, requires human intervention, lacks automation, batch processing capabilities and extraction accuracy. Therefore, this study proposed to develop a method to quantify both GV and standing dead matter (SDM) fraction cover from field-taken digital RGB images with semi-automated batch processing capabilities (i.e., written as a python script) for mixed grasslands with more complex background information including litter, moss, lichen, rocks and soil. The results show that the GV cover extracted by the method developed in this study is superior to that by subjective visual estimation based on the linear relation with normalized vegetation index (NDVI) calculated from field measured hyper-spectra (R2 = 0.846, p < 0.001 for GV cover estimated from RGB images; R2 = 0.711, p < 0.001 for subjective visual estimated GV cover). The results also show that the developed method has great potential to estimate SDM cover with limited effects of light colored understory components including litter, soil crust and bare soil. In addition, the results of this study indicate that subjective visual estimation tends to estimate higher cover for both GV and SDM compared to that estimated from RGB images. Full article
(This article belongs to the Special Issue Remote Sensing Application for Monitoring Grassland)
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19 pages, 2273 KiB  
Article
Monitoring and Landscape Dynamic Analysis of Alpine Wetland Area Based on Multiple Algorithms: A Case Study of Zoige Plateau
by Wenlong Li, Pengfei Xue, Chenli Liu, Hepiao Yan, Gaofeng Zhu and Yapeng Cao
Sensors 2020, 20(24), 7315; https://doi.org/10.3390/s20247315 - 19 Dec 2020
Cited by 28 | Viewed by 3684
Abstract
As an important part of the wetland ecosystem, alpine wetland is not only one of the most important ecological water conservation areas in the Qinghai–Tibet Plateau region, but is also an effective regulator of the local climate. In this study, using three machine [...] Read more.
As an important part of the wetland ecosystem, alpine wetland is not only one of the most important ecological water conservation areas in the Qinghai–Tibet Plateau region, but is also an effective regulator of the local climate. In this study, using three machine learning algorithms to extract wetland, we employ the landscape ecological index to quantitatively analyze the evolution of landscape patterns and grey correlation to analyze the driving factors of Zoige wetland landscape pattern change from 1995 to 2020. The following results were obtained. (1) The random forest algorithm (RF) performs best when dealing with high-dimensional data, and the accuracy of the decision tree algorithm (DT) is better. The performance of the RF and DT is better than that of the support vector machine algorithm. (2) The alpine wetland in the study area was degraded from 1995 to 2015, whereas wetland area began to increase after 2015. (3) The results of landscape analysis show the decrease in wetland area from 1995 to 2005 was mainly due to the fragmentation of larger patches into many small patches and loss of the original small patches, while the 2005 to 2015 decrease was caused by the loss of many middle patches and the decrease in large patches from the edge to the middle. The 2015 to 2020 increase is due to an increase in the number of smaller patches and recovery of original wetland area. (4) The grey correlation degree further shows that precipitation and evaporation are the main factors leading to the change in the landscape pattern of Zoige alpine wetland. The results are of great significance to the long-term monitoring of the Zoige wetland ecosystem. Full article
(This article belongs to the Special Issue Remote Sensing Application for Monitoring Grassland)
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26 pages, 3579 KiB  
Article
Identification of the Optimal Season and Spectral Regions for Shrub Cover Estimation in Grasslands
by Irini Soubry and Xulin Guo
Sensors 2021, 21(9), 3098; https://doi.org/10.3390/s21093098 - 29 Apr 2021
Cited by 13 | Viewed by 3348
Abstract
Woody plant encroachment (WPE), the expansion of native and non-native trees and shrubs into grasslands, is a less studied factor that leads to declines in grassland ecosystem health. With the increasing application of remote sensing in grassland monitoring and measuring, it is still [...] Read more.
Woody plant encroachment (WPE), the expansion of native and non-native trees and shrubs into grasslands, is a less studied factor that leads to declines in grassland ecosystem health. With the increasing application of remote sensing in grassland monitoring and measuring, it is still difficult to detect WPE at its early stages when its spectral signals are not strong enough. Even at late stages, woody species have strong vegetation characteristics that are commonly categorized as healthy ecosystems. We focus on how shrub encroachment can be detected through remote sensing by looking at the biophysical and spectral properties of the WPE grassland ecosystem, investigating the appropriate season and wavelengths that identify shrub cover, testing the spectral separability of different shrub cover groups and by revealing the lowest shrub cover that can be detected by remote sensing. Biophysical results indicate spring as the best season to distinguish shrubs in our study area. The earliest shrub encroachment can be identified most likely only when the cover reaches between 10% and 25%. A correlation between wavelength spectra and shrub cover indicated four regions that are statistically significant, which differ by season. Furthermore, spectral separability of shrubs increases with their cover; however, good separation is only possible for pure shrub pixels. From the five separability metrics used, Transformed divergence and Jeffries-Matusita distance have better interpretations. The spectral regions for pure shrub pixel separation are slightly different from those derived by correlation and can be explained by the influences from land cover mixtures along our study transect. Full article
(This article belongs to the Special Issue Remote Sensing Application for Monitoring Grassland)
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15 pages, 6798 KiB  
Article
Extracting Fractional Vegetation Cover from Digital Photographs: A Comparison of In Situ, SamplePoint, and Image Classification Methods
by Xiaolei Yu and Xulin Guo
Sensors 2021, 21(21), 7310; https://doi.org/10.3390/s21217310 - 3 Nov 2021
Cited by 11 | Viewed by 3401
Abstract
Fractional vegetation cover is a key indicator of rangeland health. However, survey techniques such as line-point intercept transect, pin frame quadrats, and visual cover estimates can be time-consuming and are prone to subjective variations. For this reason, most studies only focus on overall [...] Read more.
Fractional vegetation cover is a key indicator of rangeland health. However, survey techniques such as line-point intercept transect, pin frame quadrats, and visual cover estimates can be time-consuming and are prone to subjective variations. For this reason, most studies only focus on overall vegetation cover, ignoring variation in live and dead fractions. In the arid regions of the Canadian prairies, grass cover is typically a mixture of green and senescent plant material, and it is essential to monitor both green and senescent vegetation fractional cover. In this study, we designed and built a camera stand to acquire the close-range photographs of rangeland fractional vegetation cover. Photographs were processed by four approaches: SamplePoint software, object-based image analysis (OBIA), unsupervised and supervised classifications to estimate the fractional cover of green vegetation, senescent vegetation, and background substrate. These estimates were compared to in situ surveys. Our results showed that the SamplePoint software is an effective alternative to field measurements, while the unsupervised classification lacked accuracy and consistency. The Object-based image classification performed better than other image classification methods. Overall, SamplePoint and OBIA produced mean values equivalent to those produced by in situ assessment. These findings suggest an unbiased, consistent, and expedient alternative to in situ grassland vegetation fractional cover estimation, which provides a permanent image record. Full article
(This article belongs to the Special Issue Remote Sensing Application for Monitoring Grassland)
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