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Remote Sensing Application for Monitoring Grassland

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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 11393

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Prof. Dr. Xulin Guo

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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

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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

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Keywords

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

Published Papers (4 papers)

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15 pages, 6798 KiB

Open AccessArticle

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 8 | Viewed by 2474

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 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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19 pages, 2273 KiB

Open AccessArticle

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 25 | Viewed by 3045

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 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

Open AccessArticle

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 10 | Viewed by 2754

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 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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17 pages, 8928 KiB

Open AccessArticle

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 2441

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 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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