Spatial variations in salinity stress across a coastal landscape using vegetation indices derived from hyperspectral imagery

The timely monitoring of crop disease development is very important for precision agriculture applications. Remote sensing-based vegetation indices (VIs) can be good indicators of crop disease severity, but current methods are mainly dependent on manual ground survey results. Based on VI normalization, an automated crop disease severity grading method without the use of ground surveys was proposed in this study. This technique was applied to two cotton fields infested with different levels of cotton root rot in south Texas in the United States, where airborne hyperspectral imagery was collected. Six typical VIs were calculated from the hyperspectral imagery and their histograms indicated that VI normalization could eliminate the influences of variable field conditions and the VI value range variations, allowing a potentially broader scope of application. According to the analysis of the obtained results from the spectral dimension, spatial dimension and descriptive statistics, the disease grading results were in general agreement with previous ground survey results, proving the validity of the disease severity grading method. Although satisfactory results could be achieved from different types of VI, there is still room for further improvement through the exploration of more VIs. With the advantages of independence of ground surveys and potential universal applicability, the newly proposed crop disease grading method will be of great significance for crop disease monitoring over large geographical areas.

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Mapping Spatial Variations of Land Cover in a Coastal Landscape Using Pattern Metrics

Procedia - Social and Behavioral Sciences ◽

10.1016/j.sbspro.2014.02.077 ◽

2014 ◽

Vol 120 ◽

pp. 23-30 ◽

Cited By ~ 3

Author(s):

Hakan Alphan ◽

Nil Çelik

Keyword(s):

Land Cover ◽

Spatial Variations ◽

Coastal Landscape ◽

Pattern Metrics

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Evaluating annual spruce budworm defoliation using change detection of vegetation indices calculated from satellite hyperspectral imagery

Remote Sensing of Environment ◽

10.1016/j.rse.2020.112204 ◽

2021 ◽

Vol 253 ◽

pp. 112204

Author(s):

Shawn D. Donovan ◽

David A. MacLean ◽

Yun Zhang ◽

Michael B. Lavigne ◽

John A. Kershaw

Keyword(s):

Change Detection ◽

Hyperspectral Imagery ◽

Vegetation Indices ◽

Spruce Budworm

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Combining vegetation indices, constrained ordination and fuzzy classification for mapping semi-natural vegetation units from hyperspectral imagery

Remote Sensing of Environment ◽

10.1016/j.rse.2010.01.003 ◽

2010 ◽

Vol 114 (6) ◽

pp. 1155-1166 ◽

Cited By ~ 62

Author(s):

Jens Oldeland ◽

Wouter Dorigo ◽

Lena Lieckfeld ◽

Arko Lucieer ◽

Norbert Jürgens

Keyword(s):

Hyperspectral Imagery ◽

Vegetation Indices ◽

Natural Vegetation ◽

Fuzzy Classification ◽

Constrained Ordination

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A Review of Remote Sensing Challenges for Food Security with Respect to Salinity and Drought Threats

Remote Sensing ◽

10.3390/rs13010006 ◽

2020 ◽

Vol 13 (1) ◽

pp. 6

Author(s):

Wen Wen ◽

Joris Timmermans ◽

Qi Chen ◽

Peter M. van Bodegom

Keyword(s):

Remote Sensing ◽

Food Security ◽

Salinity Stress ◽

Vegetation Indices ◽

Crop Productivity ◽

Stress Monitoring ◽

Multiple Traits ◽

Global Food Security ◽

The Impact ◽

Crop Health

Drought and salinity stress are considered to be the two main factors limiting crop productivity. With climate change, these stresses are projected to increase, further exacerbating the risks to global food security. Consequently, to tackle this problem, better agricultural management is required on the basis of improved drought and salinity stress monitoring capabilities. Remote sensing makes it possible to monitor crop health at various spatiotemporal scales and extents. However, remote sensing has not yet been used to monitor both drought and salinity stresses simultaneously. The aim of this paper is to review the current ability of remote sensing to detect the impact of these stresses on vegetation indices (VIs) and crop trait responses. We found that VIs are insufficiently accurate (0.02 ≤ R2 ≤ 0.80) to characterize the crop health under drought and salinity stress. In contrast, we found that plant functional traits have a high potential to monitor the impacts of such stresses on crop health, as they are more in line with the vegetation processes. However, we also found that further investigations are needed to achieve this potential. Specifically, we found that the spectral signals concerning drought and salinity stress were inconsistent for the various crop traits. This inconsistency was present (a) between studies utilizing similar crops and (b) between investigations studying different crops. Moreover, the response signals for joint drought and salinity stress overlapped spectrally, thereby significantly limiting the application of remote sensing to monitor these separately. Therefore, to consistently monitor crop responses to drought and salinity, we need to resolve the current indeterminacy of the relationships between crop traits and spectrum and evaluate multiple traits simultaneously. Using radiative transfer models (RTMs) and multi-sensor frameworks allow monitoring multiple crop traits and may constitute a way forward toward evaluating drought and salinity impacts.

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