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

Govender, Moreshnee; Chetty, K.T.; Naiken, Vivek; Bulcock, H. H.

doi:10.4314/wsa.v34i2.183634

Cited by 64 publications

(23 citation statements)

References 18 publications

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“…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%

Mapping Foliar Nutrition Using WorldView-3 and WorldView-2 to Assess Koala Habitat Suitability

Levin

Seabrook

et al. 2019

Remote Sensing

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Conservation planning and population assessment for widely-distributed, but vulnerable, arboreal folivore species demands cost-effective mapping of habitat suitability over large areas. This study tested whether multispectral data from WorldView-3 could be used to estimate and map foliar digestible nitrogen (DigN), a nutritional measure superior to total nitrogen for tannin-rich foliage for the koala (Phascolarctos cinereus). We acquired two WorldView-3 images (November 2015) and collected leaf samples from Eucalyptus woodlands in semi-arid eastern Australia. Linear regression indicated the normalized difference index using bands “Coastal” and “NIR1” best estimated DigN concentration (% dry matter, R2 = 0.70, RMSE = 0.19%). Foliar DigN concentration was mapped for multi-species Eucalyptus open woodlands across two landscapes using this index. This mapping method was tested on a WorldView-2 image (October 2012) with associated koala tracking data (August 2010 to November 2011) from a different landscape of the study region. Quantile regression showed significant positive relationship between estimated DigN and occurrence of koalas at 0.999 quantile (R2 = 0.63). This study reports the first attempt to use a multispectral satellite-derived spectral index for mapping foliar DigN at a landscape-scale (100s km2). The mapping method can potentially be incorporated in mapping and monitoring koala habitat suitability for conservation management.

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Section: Introductionmentioning

confidence: 99%

Mapping Foliar Nutrition Using WorldView-3 and WorldView-2 to Assess Koala Habitat Suitability

Levin

Seabrook

et al. 2019

Remote Sensing

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“…(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%

Suitability of resampled multispectral datasets for mapping flowering plants in the Kenyan savannah

Makori

Abdel‐Rahman

Landmann

et al. 2020

Preprint

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Pollination services and honeybee health in general are important in the African savannahs particularly to farmers who often rely on honeybee products as a supplementary source of income. Therefore, it is imperative to understand the floral cycle, abundance and spatial 5 distribution of melliferous plants in the African savannah landscapes. Furthermore, placement of apiaries in the landscapes could benefit from information on spatiotemporal patterns of flowering plants, by optimising honeybees' foraging behaviours, which could improve apiary productivity. This study sought to assess the suitability of simulated multispectral data for mapping melliferous (flowering) plants in the African savannahs. Bi-temporal AISA Eagle 10 hyperspectral images, resampled to four sensors (i.e. WorldView-2, RapidEye, Spot-6 and Sentinel-2) spatial and spectral resolutions, and a 10-cm ultra-high spatial resolution aerial imagery coinciding with onset and peak flowering periods were used in this study. Ground reference data was collected at the time of imagery capture. The advanced machine learning random forest (RF) classifier was used to map the flowering plants at a landscape scale and a 15 classification accuracy validated using 30% independent test samples. The results showed that 93.33%, 69.43%, 67.52% and 82.18% accuracies could be achieved using WorldView-2, RapidEye, Spot-6 and Sentinel-2 data sets respectively, at the peak flowering period. Our study provides a basis for the development of operational and cost-effective approaches for mapping flowering plants in an African semiarid agroecological landscape. Specifically, such mapping 20 approaches are valuable in providing timely and reliable advisory tools for guiding the implementation of beekeeping systems at a landscape scale.Keywords: Melliferous plants, beehealth, AISA Eagle, resampled multispectral data sets, random forest 45 multicollinearity, especially when using conventional parametric classification and regression procedures often make the use of hyperspectral data unfeasible. Specifically, the Hughes effect and the high redundancy rates of some bands in models developed using hyperspectral data impede landscape modelling (13)(14)(15)(16). In this regard, it is paramount to explore the utility of multispectral images with fewer broad bands for optimal discrimination of flowering plant 50 species (9,(17)(18)(19). However, whereas broadband multispectral data of medium spatial resolution such as the Landsat series have become popular in landscape mapping, they could mask out specific spectral features of flowering plant species, resulting in very low mapping accuracy. The relatively newly launched higher spectral and/or spatial resolution sensors such as WorldView-2, RapidEye, Spot-6 and Sentinel-2 offer great potential in detecting flowering 55 plants (20)(21)(22). Such sensors are specifically designed to capture spectral properties at additional wavebands such as red-edge and yellow spectrum that mimic over 90% of plant biophysiological information (23-25). Ho...

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“…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%

Use of visible spectrum sUAS photography for land cover classification at nest sites of a declining bird species (Falco sparverius)

Kamm

Reed

2019

Remote Sens Ecol Conserv

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Photography with small unmanned aircraft systems (sUAS) offers opportunities for researchers to better understand habitat selection in wildlife, especially for species that select habitat from an aerial perspective (e.g., many bird species). The growing number of commercial sUAS being flown by recreational users represents a potentially valuable source of data for documenting and studying wildlife habitat. We used a commercially available quadcopter sUAS with a visible spectrum camera to classify habitat for American Kestrels (Falco sparverius; Aves), as well as to evaluate aspects of image processing and postprocessing relevant to a simple habitat analysis using citizen science photography. We investigated inter–observer repeatability of habitat classification, effectiveness of cross‐image classification and Gaussian filtering, and sensitivity to classification resolution. We photographed vegetation around nests from both 25 m and 50 m above takeoff elevation, and analyzed images via maximum likelihood supervised classification. Our results indicate that commercial off‐the‐shelf sUAS photography can distinguish between grass, herbaceous, woody, bare ground, and human‐modified cover classes with good (kappa > 0.6) or strong (kappa > 0.8) accuracy using a 0.25 m2 minimum patch size for aggregation. There was inter‐subject variability in designating training samples, but high repeatability of supervised classification accuracy. Gaussian filtering reduced classification accuracy, while coarser classification resolution out‐performed finer resolution due to “speckling noise.” Image self‐classification significantly outperformed cross‐image classification. Mean classification accuracy metrics (kappa values) across different photo heights differed little, but, importantly, the rank order of images differed noticeably.

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

Cited by 64 publications

References 18 publications

Mapping Foliar Nutrition Using WorldView-3 and WorldView-2 to Assess Koala Habitat Suitability

Mapping Foliar Nutrition Using WorldView-3 and WorldView-2 to Assess Koala Habitat Suitability

Suitability of resampled multispectral datasets for mapping flowering plants in the Kenyan savannah

Use of visible spectrum sUAS photography for land cover classification at nest sites of a declining bird species (Falco sparverius)

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