Review On Conventional and Advanced Classification Approaches in Remote Sensing Image Processing

Shivakanth, Gandla; Tanwar, Prakash Singh

doi:10.26438/ijcse/v6i11.871879

Cited by 4 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fortunately, even with mixed pixels, there exist a number of spectral unmixing techniques that could enhance WPE mapping with coarser resolution pixels [ 80 ]. With spectral unmixing, each pixel gets assigned to fractions of its including classes, which are defined by endmembers [ 81 ].…”

Section: Resultsmentioning

confidence: 99%

Identification of the Optimal Season and Spectral Regions for Shrub Cover Estimation in Grasslands

Soubry

Guo

2021

Sensors

View full text Add to dashboard Cite

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.

show abstract

Section: Resultsmentioning

confidence: 99%