2019
DOI: 10.4314/wsa.v34i2.183634
|View full text |Cite
|
Sign up to set email alerts
|

A comparison of satellite hyperspectral and multispectral remote sensing imagery for improved classification and mapping of vegetation

Abstract: In recent years the use of remote sensing imagery to classify and map vegetation over different spatial scales has gained wide acceptance in the research community. Many national and regional datasets have been derived using remote sensing data. However, much of this research was undertaken using multispectral remote sensing datasets. With advances in remote sensing technologies, the use of hyperspectral sensors which produce data at a higher spectral resolution is being investigated. The aim of this study was… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1
1

Citation Types

1
21
0
1

Year Published

2019
2019
2023
2023

Publication Types

Select...
8
1

Relationship

0
9

Authors

Journals

citations
Cited by 64 publications
(23 citation statements)
references
References 18 publications
1
21
0
1
Order By: Relevance
“…Determining foliar quality over large areas using field-sampling and chemical analysis involves very large sample sizes and is unavoidably expensive [17][18][19]. Remote sensing applications offer new opportunities to address this problem by providing proximal data for estimating plant chemistry [20].…”
Section: Introductionmentioning
confidence: 99%
“…Determining foliar quality over large areas using field-sampling and chemical analysis involves very large sample sizes and is unavoidably expensive [17][18][19]. Remote sensing applications offer new opportunities to address this problem by providing proximal data for estimating plant chemistry [20].…”
Section: Introductionmentioning
confidence: 99%
“…(2015) noted that spectral resolution improves the ability of an image to identify finer vegetation features like the colour and flower compaction. However, we acknowledge that spatial resolution is complimentary to spectral resolution when mapping features such as flowering since flower blooms are more often smaller in size compared to other background features (56,57). It would therefore be expected that an image with higher spatial resolution would yield higher accuracy when coupled with higher spectral resolution.…”
Section: Discussionmentioning
confidence: 99%
“…After postprocessing, we created a confusion matrix for each image, which compares the computer's pixel classifications within the user‐defined test samples to the ground truth of those samples (Congalton ; Underwood ; Govender et al. ). The confusion matrix returns information for each image on how many pixels within test samples were correctly identified, as well as how many pixels were misidentified as other cover types (i.e., the degree to which the classifier was ‘confused’).…”
Section: Methodsmentioning
confidence: 99%