Detecting Sirex noctilio grey-attacked and lightning-struck pine trees using airborne hyperspectral data, random forest and support vector machines classifiers

Abdel‐Rahman, Elfatih M.; Mutanga, Onisimo; Adam, Elhadi; Ismail, Riyad

doi:10.1016/j.isprsjprs.2013.11.013

Cited by 148 publications

(86 citation statements)

References 63 publications

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“…VI is calculated for each waveband, used for node splitting at a given tree, and then averaged across all trees to produce the final VI per waveband [30]. Similar to [23,24], the top 10% (p = 18) of the ranked waveband importance as determined by RF and XGBoost was used to create a subset of important wavebands. RF and XGBoost models were produced for both the original dataset and the subset of 18 wavebands.…”

Section: Dimensionality Reductionmentioning

confidence: 99%

Modelling Water Stress in a Shiraz Vineyard Using Hyperspectral Imaging and Machine Learning

et al. 2018

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Abstract:The detection of water stress in vineyards plays an integral role in the sustainability of high-quality grapes and prevention of devastating crop loses. Hyperspectral remote sensing technologies combined with machine learning provides a practical means for modelling vineyard water stress. In this study, we applied two ensemble learners, i.e., random forest (RF) and extreme gradient boosting (XGBoost), for discriminating stressed and non-stressed Shiraz vines using terrestrial hyperspectral imaging. Additionally, we evaluated the utility of a spectral subset of wavebands, derived using RF mean decrease accuracy (MDA) and XGBoost gain. Our results show that both ensemble learners can effectively analyse the hyperspectral data. When using all wavebands (p = 176), RF produced a test accuracy of 83.3% (KHAT (kappa analysis) = 0.67), and XGBoost a test accuracy of 80.0% (KHAT = 0.6). Using the subset of wavebands (p = 18) produced slight increases in accuracy ranging from 1.7% to 5.5% for both RF and XGBoost. We further investigated the effect of smoothing the spectral data using the Savitzky-Golay filter. The results indicated that the Savitzky-Golay filter reduced model accuracies (ranging from 0.7% to 3.3%). The results demonstrate the feasibility of terrestrial hyperspectral imagery and machine learning to create a semi-automated framework for vineyard water stress modelling.

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Section: Dimensionality Reductionmentioning

confidence: 99%

Modelling Water Stress in a Shiraz Vineyard Using Hyperspectral Imaging and Machine Learning

et al. 2018

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“…It is therefore expected that bands in the visible portion of the EMS were found to be more useful for feature and land cover class discrimination [26]. The SWIR region of the EMS could have been useful for discriminating different flowering plants.…”

Section: Discussionmentioning

confidence: 99%

“…In addition, the dimensionality of hyperspectral features might not be captured in a linear projection. Therefore, non-linear classification methods such as random forest, that produce variable selection as a by-product during the learning process, are considered efficient algorithms for the analyses of hyperspectral data especially in biomes where spectral mixing is highly non-linear (e.g., [21][22][23][24][25][26]). …”

Section: Introductionmentioning

confidence: 99%

The Utility of AISA Eagle Hyperspectral Data and Random Forest Classifier for Flower Mapping

et al. 2015

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Knowledge of the floral cycle and the spatial distribution and abundance of flowering plants is important for bee health studies to understand the relationship between landscape and bee hive productivity and honey flow. The key objective of this study was to show how AISA Eagle hyperspectral data and random forest (RF) can be optimally utilized to produce flowering and spatially explicit land use/land cover (LULC) maps for a study site in Kenya. AISA Eagle imagery was captured at the early flowering period (January 2014) and at the peak flowering season (February 2013). Data on white and yellow flowering trees as well as LULC classes in the study area were collected and used as ground-truth points. We utilized all 64 AISA Eagle bands and also used variable importance in RF to identify the most important bands in both AISA Eagle data sets. The results showed that flowering was most accurately mapped using the AISA Eagle data from the peak flowering period (85.71%-88.15% overall accuracy for the peak flowering OPEN ACCESSRemote Sens. 2015, 7 13299 season imagery versus 80.82%-83.67% for the early flowering season). The variable optimization (i.e., variable selection) analysis showed that less than half of the AISA bands (n = 26 for the February 2013 data and n = 21 for the January 2014 data) were important to attain relatively reliable classification accuracies. Our study is an important first step towards the development of operational flower mapping routines and for understanding the relationship between flowering and bees' foraging behavior.

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“…Random forest (RF) classifier (Breiman, 2001), have been developed and produced promising results in mapping forest health conditions and extracting forest structure parameters (Dye et al, 2012;Grinand et al, 2013;Abdel-Rahman et al, 2014). Since the textural and local spatial information has the potential to improving the accuracy of class designation by minimizing intra-class variation (Lé vesque and King, 2003;Wulder and Boots, 1998), the overall objective of this study is to assess whether the GLCM and Gi features extracted from IKONOS imagery were effective in determining Robinia pseudoacacia forest health conditions in the YRD, China using RF classifier.…”

Section: Introductionmentioning

confidence: 99%

Mapping Robinia Pseudoacacia Forest Health Conditions by Using Combined Spectral, Spatial and Textureal Information Extracted From Ikonos Imagery

Wang

Zhao

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Commission VI, WG VI/4KEY WORDS: GLCM, Getis statistic, random forest, forest health condition, Robinia pseudoacacia ABSTRACT:In this study grey-level co-occurrence matrix (GLCM) textures and a local statistical analysis Getis statistic (Gi), computed from IKONOS multispectral (MS) imagery acquired from the Yellow River Delta in China, along with a random forest (RF) classifier, were used to discriminate Robina pseudoacacia tree health levels. The different RF classification results of the three forest health conditions were created: (1) an overall accuracy (OA) of 79.5% produced using the four MS band reflectances only; (2) an OA of 97.1% created with the eight GLCM features calculated from IKONOS Band 4 with the optimal window size of 13 × 13 and direction 45°; (3) an OA of 94.0% created using the four Gi features calculated from the four IKONOS MS bands with the optimal distance value of 5 and Queen's neighborhood rule; and (4) an OA of 96.9% created with the combined 16 spectral (four), spatial (four), and textural (eight) features. The experimental results demonstrate that (a) both textural and spatial information was more useful than spectral information in determining the Robina pseudoacacia forest health conditions; and (b) IKONOS NIR band was more powerful than visible bands in quantifying varying degree of forest crown dieback.

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Detecting Sirex noctilio grey-attacked and lightning-struck pine trees using airborne hyperspectral data, random forest and support vector machines classifiers

Cited by 148 publications

References 63 publications

Modelling Water Stress in a Shiraz Vineyard Using Hyperspectral Imaging and Machine Learning

Modelling Water Stress in a Shiraz Vineyard Using Hyperspectral Imaging and Machine Learning

The Utility of AISA Eagle Hyperspectral Data and Random Forest Classifier for Flower Mapping

Mapping Robinia Pseudoacacia Forest Health Conditions by Using Combined Spectral, Spatial and Textureal Information Extracted From Ikonos Imagery

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