Quantifying Woody Plant Encroachment in Grasslands: A Review on Remote Sensing Approaches

Soubry, Irini; Guo, Xulin

doi:10.1080/07038992.2022.2039060

Cited by 10 publications

(7 citation statements)

References 280 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…WPE is a phenomenon that is decidedly complex and multifactorial, with causes including fire suppression, changing grazing regimes, shifting human behavior, and others [ 9 , 31 ]. Considerable effort has been invested in developing remote-sensing approaches to characterizing WPE processes retrospectively [ 8 , 25 , 32 ]. However, less attention has been paid to the more exploratory challenge of anticipating areas with greater or lesser potential for further WPE [ 33 , 34 ].…”

Section: Discussionmentioning

confidence: 99%

“…Numerous studies have mapped WPE on various spatial extents [ 8 , 9 ], or have characterized WPE implications for ecosystem function [ 1 – 3 ], water resources [ 10 ] and climate [ 11 ]. Although several studies have sought to identify landscape-scale drivers of WPE [ 12 , 13 ], few have taken on the harder challenge of attempting to predict the spatial course of WPE processes.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Correlative ecological niche model applications to predicting landscape-scale woody plant encroachment in Kansas tallgrass prairie systems

Peterson,

Yao,

Cobos

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

Woody plant encroachment (WPE) in grassland ecosystems has been a pervasive process across the Great Plains, yet a predictive understanding of where it will occur has been elusive. As an exploration of tools of potential utility in this challenge, we mapped WPE processes over the years 2015–2021 in a set of 9 counties in central Kansas. We developed and tested two correlative models based on landscape features: one that assessed distribution of evergreen trees in 2015, and another that assessed areas of WPE in succeeding years. Both models were successful, being able to predict 2015 forest distributions and being able to predict WPE during 2015–2021, as functions of characteristics of landscapes. These simple, correlative models will certainly not be able to predict WPE processes globally, or even regionally, but provide first proof-of-concept explorations for the central Great Plains region.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Correlative ecological niche model applications to predicting landscape-scale woody plant encroachment in Kansas tallgrass prairie systems

Peterson,

Yao,

Cobos

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…In the absence of disturbances, such as fires and grazing, woody encroachment in the crown lands would represent a secondary succession from grasses to shrubs (Schütz, 2010). The rise in temperatures and increasing occurrence of droughts associated with climate change can contribute to woody encroachment in grasslands (Soubry & Guo, 2022). Therefore, timely and accurate monitoring would be beneficial for implementing effective management practices to address future extreme events.…”

Section: Discussionmentioning

confidence: 99%

“…Data fusion in remote sensing commonly refers to the integration of data collected from multiple sensors (Fassnacht et al, 2016; Soubry & Guo, 2022). In the MGI's case, it used the active SAR data from Sentinel‐1 and passive optical data from Sentinel‐2.…”

Section: Discussionmentioning

confidence: 99%

“…Active SAR sensors, such as Sentinel‐1, measure the strength of backscatter energy from the surface under virtually all weather and light conditions (Ali et al, 2016; Lillesand et al, 2015). Furthermore, higher surface roughness indicates denser vegetation cover, which returns a higher backscatter signal (Soubry & Guo, 2022). Considering this, Sentinel‐1 can provide structural and canopy height information, which is beneficial in the discrimination of vegetation types (e.g., trees, shrubs and grasses).…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Assessment of remotely sensed inventories for land cover classification of public grasslands in Manitoba, Canada

Encabo,

Cordeiro,

Badreldin

et al. 2023

Grass and Forage Science

View full text Add to dashboard Cite

Land cover classification is one of the most common applications of remote sensing and is used for developing and modifying land management policies on agricultural landscapes to achieve conservation and economic goals, such as reducing grassland degradation and improving livestock and crop production. In this study, the grassland classification of the crown lands (public grasslands in Canada) from a newly developed remotely sensed dataset in the Prairie Province of Manitoba (i.e., the Manitoba Grassland Inventory, MGI) was assessed in terms of accuracy by comparison to non‐spatial government records. The analysis consisted of (i) converting non‐spatial records from the provincial crown land database to spatially‐defined parcels by performing parcel delineations using geographic information system (GIS) and R programming tools, (ii) summarising the MGI classification at the same spatial scale, and (iii) comparing the agreement between MGI and the crown land database. The most common land cover types identified were: forest (30%) and shrubland (25%), followed by native (10%) and tame (9%) grasslands. However, the class agreements between woody (i.e., forests and shrublands) and grassy (i.e., native and tame grasslands) vegetation classes were low between these datasets because of their spectral similarities. Based on these results, we suggest additional refinements on both sensor and ground data to improve the classification agreement between these datasets. This study is one of the first attempts to compare ground‐collected government records against a remotely sensed product in Manitoba.

show abstract

A bibliometric analysis for remote sensing applications in bush encroachment mapping of grassland and savanna ecosystems

Gcayi,

Adelabu,

Nduku

et al. 2024

Appl Geomat

View full text Add to dashboard Cite

Grasslands and savannas are experiencing transformation and degradation due to bush encroachment (BE). BE has been monitored using restrictive traditional techniques that include field surveys and manual long-term observations. Owing to the limitations of traditional techniques, remote sensing (RS) is an attractive alternative to assess BE because of its generally high precision and return interval, cost-effectiveness, and availability of historical data archives. Furthermore, RS has an added advantage in its ability of acquiring global coherent data in near-real time compared to the snapshot acquisition mode with traditional surveying techniques. Despite its extensive application and vast possibilities, a critical synthesis for RS successes, shortcomings, and best practices in mapping BE in savannas and grasslands is lacking. Thus, broadly, the direction, which this type of investigation has taken over the years is largely unknown. This study sought to connect and measure the progress RS has made in mapping BE in grassland and savanna ecosystems through bibliometric analysis. One hundred and twenty-three peer-reviewed English written documents from the Web of Science and Scopus databases were evaluated. The study revealed 13.05% average annual publication growth, indicating that RS and BE mapping research in grasslands and savannas has been increasing over the survey period. Most published studies came from the USA, while the rest came from South Africa, China, and Australia. The results indicate that BE has been extensively mapped in grasslands and savannas using coarse to medium resolution data. As a result, there is a weak relationship (r² = 0.324) between the dependent variable (aerial images) and the independent variable (percentage of woody cover). This connotes the need to improve BE assessments in grasslands and savannas by integrating recent high-resolution data, machine learning algorithms and artificial intelligence.

show abstract

Quantifying Woody Plant Encroachment in Grasslands: A Review on Remote Sensing Approaches

Cited by 10 publications

References 280 publications

Correlative ecological niche model applications to predicting landscape-scale woody plant encroachment in Kansas tallgrass prairie systems

Correlative ecological niche model applications to predicting landscape-scale woody plant encroachment in Kansas tallgrass prairie systems

Assessment of remotely sensed inventories for land cover classification of public grasslands in Manitoba, Canada

A bibliometric analysis for remote sensing applications in bush encroachment mapping of grassland and savanna ecosystems

Contact Info

Product

Resources

About