Assessing the potential of remote sensing to discriminate invasive &lt;i&gt;Seriphium plumosum&lt;/i&gt; from grass

Dubula, Bambo; Tefsamichael, Solomon Gebremariam; Rampedi, Isaac T.

doi:10.4314/sajg.v5i2.8

Cited by 5 publications

(2 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Their research showed some parts of the study area had constantly been burnt with high fire severity over 20 years. These parts of the study area may be experiencing changes in species composition, leaving only the fire-tolerant species or invasive species like S. plumosum, which is a known species that promote the spread of wildfires (52)(53)(54). In fires recently disturbed areas in the park, S. plumosum can sprout and lie dormant when encountering higher temperatures and lowmoisture conditions for the remainder of winter while awaiting the emergence of spring (55).…”

Section: Figure 11: Species Contributions To Change In Phragmites Australismentioning

confidence: 99%

Mapping Vegetation Species Succession in a Mountainous Grassland ecosystem using Landsat and Sentinel-2 data

Adagbasa

Mukwada

2021

Preprint

View full text Add to dashboard Cite

Vegetation species succession and composition are significant factors determining the rate of ecosystem biodiversity recovery after being disturbed and subsequently vital for sustainable and effective natural resource management and biodiversity. The succession and composition of grasslands ecosystems worldwide have significantly been affected by accelerated environmental changes due to natural and anthropogenic activities. Therefore, understanding spatial data on the succession of grassland vegetation species and communities through mapping and monitoring is essential to gain knowledge on the ecosystem and other ecosystem services. This study used a random forest machine learning classifier on the Google Earth Engine platform to classify grass vegetation species with Landsat 7 ETM+ and ASTER multispectral imager (MI) data resampled with the current Sentinel-2 MSI data to map and estimate the changes in vegetation species succession. The results indicate that ASTER IM has the least accuracy of 72%, Landsat 7 ETM+ 84%, and Sentinel-2 had the highest of 87%. The result also shows that other species had replaced four dominant grass species totaling about 49 km 2 throughout the study.

show abstract

Section: Figure 11: Species Contributions To Change In Phragmites Australismentioning

confidence: 99%

Mapping Vegetation Species Succession in a Mountainous Grassland ecosystem using Landsat and Sentinel-2 data

Adagbasa

Mukwada

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…In other cases, on-ground reference data acquisition is necessary, especially for herbaceous plants characterized by high phenological variability and growing in heterogenous, species-rich ecosystems. This latter kind of reference data acquisition is often labor-intensive, expensive, and sometimes not feasible due to land inaccessibility [12,13]. The percentage cover of herbaceous plants in natural ecosystems changes significantly over intra- and inter-annual time periods.…”

Section: Introductionmentioning

confidence: 99%

Using Airborne Hyperspectral Imaging Spectroscopy to Accurately Monitor Invasive and Expansive Herb Plants: Limitations and Requirements of the Method

Kopeć

Zakrzewska

Halladin-Dabrowska

et al. 2019

Sensors

View full text Add to dashboard Cite

Remote sensing (RS) is currently regarded as one of the standard tools used for mapping invasive and expansive plants for scientific purposes and it is increasingly widely used in nature conservation management. The applicability of RS methods is determined by its limitations and requirements. One of the most important limitations is the species percentage cover at which the classification result is correct and useful for nature conservation. The primary objective, carried out in 2017 in three areas of Poland, was to determine the minimum percentage cover from which it is possible to identify a target species by RS methods. A secondary objective of this research, related to the requirements of the method, was to optimize the set of training polygons for a target species in terms of the number of polygons and abundance percentage cover of the target species. Our method has to be easy to use, effective, and applicable, therefore the analysis was carried out using the basic set of rasters—the first 30 channels after the Minimum Noise Fraction (MNF) transformation (the mosaic of hyperspectral data from HySpex sensors with spectral range 0.4–2.5 µm) and commonly used Random Forest algorithm. The analysis used airborne hyperspectral data with a spatial resolution of 1 m to perform classification of one invasive and three expansive plants—two grasses and two large perennials. On-ground training and validation data sets were collected simultaneously with airborne data collection. When testing different classification scenarios, only the set of training polygons for a target species was changed. Classification results were evaluated based on three methods: accuracy measures (Kappa and F1), true-positive pixels in subclasses with different species cover and compatibility with field mapping. The classification results indicate that to classify the target plant species at the accepted level, the training dataset should contain polygons with a species cover ranging from 80–100%. Training performed only using polygons with a species characterized by a variable, but lower, cover (20–70%) and missing samples in the 80–100% range, led to a map which was not acceptable because of a high overestimation of target species. We achieved effective identification of species in areas where the species cover is above 50%, considering that ecosystems are heterogeneous. The results of these studies developed a methodology of field data acquisition and the necessity of synchronization in the acquisition of airborne data, and training and validation of on-ground sampling.

show abstract

Ecological Vulnerability Assessment to Grassland Fires in a Protected Mountainous Area Using Remote Sensing and GIS

Adagbasa

Adelabu

Okello

2022

Remote Sensing of African Mountains

View full text Add to dashboard Cite

Assessing the potential of remote sensing to discriminate invasive <i>Seriphium plumosum</i> from grass

Abstract: The usefulness of remote sensing to discriminate Seriphium plumosum from grass using a field spectrometer data was investigated in this study.

Cited by 5 publications

References 29 publications

Mapping Vegetation Species Succession in a Mountainous Grassland ecosystem using Landsat and Sentinel-2 data

Mapping Vegetation Species Succession in a Mountainous Grassland ecosystem using Landsat and Sentinel-2 data

Using Airborne Hyperspectral Imaging Spectroscopy to Accurately Monitor Invasive and Expansive Herb Plants: Limitations and Requirements of the Method

Ecological Vulnerability Assessment to Grassland Fires in a Protected Mountainous Area Using Remote Sensing and GIS

Contact Info

Product

Resources

About