The Experts below are selected from a list of 5070 Experts worldwide ranked by ideXlab platform
Alexander Brenning - One of the best experts on this subject based on the ideXlab platform.
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Classifying Fruit-Tree Crops by Landsat-8 time series
2017 First IEEE International Symposium of Geoscience and Remote Sensing (GRSS-CHILE), 2017Co-Authors: Marco A. Peña, Alexander Brenning, Renfang LiaoAbstract:Landsat-8 time series were used to classify major Crops types in Maipo and Aconcagua valleys, central Chile. In the former valley four Fruit-Tree Crops were classified applying different machine learning techniques on feature sets comprising typical index-based temporal profiles, like those using the normalized difference vegetation index, and the complete spectral resolution of the time series. In the latter valley six Fruit-Tree Crops were classified only by LDA (linear discriminant analysis), found the best performing classifier for the Maipo Valley. LDA was applied on the complete spectral resolution of the time series and on a feature set adding all possible NDIs (normalized difference indices) that can be constructed from the time series. Regardless of the feature set used good MERs (misclassification error rates) were found (≤ 0.21) for the Maipo's Crops, but they were reduced by 4 and 13 percentage points, depending on the classifier and the training sample size used, when using the complete spectral resolution of the time series. We further explored these findings in the Aconcagua Valley, where MERs were reduced from 0.13 to 0.1 when the NDI-based feature set was used. In both study cases, the most predictive bands belonged to the first image dates of the time series, corresponding to the Crops' greenup stage, and they were placed not only on the typical greenness spectral region but also on the shortwave infrared region.
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Using spectrotemporal indices to improve the Fruit-Tree crop classification accuracy
ISPRS Journal of Photogrammetry and Remote Sensing, 2017Co-Authors: Marco A. Peña, Renfang Liao, Alexander BrenningAbstract:Abstract This study assesses the potential of spectrotemporal indices derived from satellite image time series (SITS) to improve the classification accuracy of Fruit-Tree Crops. Six major Fruit-Tree crop types in the Aconcagua Valley, Chile, were classified by applying various linear discriminant analysis (LDA) techniques on a Landsat-8 time series of nine images corresponding to the 2014–15 growing season. As features we not only used the complete spectral resolution of the SITS, but also all possible normalized difference indices (NDIs) that can be constructed from any two bands of the time series, a novel approach to derive features from SITS. Due to the high dimensionality of this “enhanced” feature set we used the lasso and ridge penalized variants of LDA (PLDA). Although classification accuracies yielded by the standard LDA applied on the full-band SITS were good (misclassification error rate, MER = 0.13), they were further improved by 23% (MER = 0.10) with ridge PLDA using the enhanced feature set. The most important bands to discriminate the Crops of interest were mainly concentrated on the first two image dates of the time series, corresponding to the Crops’ greenup stage. Despite the high predictor weights provided by the red and near infrared bands, typically used to construct greenness spectral indices, other spectral regions were also found important for the discrimination, such as the shortwave infrared band at 2.11–2.19 μm, sensitive to foliar water changes. These findings support the usefulness of spectrotemporal indices in the context of SITS-based crop type classifications, which until now have been mainly constructed by the arithmetic combination of two bands of the same image date in order to derive greenness temporal profiles like those from the normalized difference vegetation index.
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Assessing Fruit-Tree crop classification from Landsat-8 time series for the Maipo Valley, Chile
Remote Sensing of Environment, 2015Co-Authors: M.a. Peña, Alexander BrenningAbstract:Abstract Satellite image time series (SITS) provide spectral–temporal features that describe phenological changes in vegetation over the growing season, which is expected to facilitate the classification of crop types. While most SITS-based crop type classifications were focused on NDVI (normalized difference vegetation index) temporal profiles, less attention has been paid to using the complete image spectral resolution of the time series. In this work we assessed different approaches to SITS-based classification of four major Fruit-Tree Crops in the Maipo Valley, central Chile, during the 2013–14 growing season. We compared four feature sets from a time series comprised of eight cloud-free Landsat-8 images: the full-band SITS, the NDVI and NDWI (normalized difference water index) temporal profiles, and an image stack with all the feature sets combined. State-of-the-art classifiers (linear discriminant analysis, LDA; random forest; and support vector machine) were applied on each feature set at different training sample sizes ( N = 100, 200, 400, 800 and 2291 fields), and classification results were assessed by cross-validation of the misclassification error rate (MER). For all the feature sets overall results were good (MERs ≤ 0.21) although substantially improved classification accuracies were achieved when the full-band SITS was employed (MER 0.14–0.05). Classifications applied on the NDVI temporal profile consistently had the worst performance. For a sample size of 200 fields, LDA using the full-band SITS of image dates 1, 3, 6 and 8 produced the best tradeoff between the number of images and classification accuracy (MER = 0.06), being the green, red, blue and SWIR (short-wave infrared) bands of image date 1 (acquired at the early greenup stage) the most relevant for crop type discrimination. Our results show the importance of considering the complete image spectral resolution for SITS-based crop type classifications as the commonly used NDVI temporal profile and their red and near infrared bands were not found the most significant to discriminate the crop types of interest. Furthermore, in light of the good results obtained, the methodology used here might be transferred to similar agricultural lands cultivated with the same crop types, thus providing a reliable and relatively efficient methodology for creating and updating crop inventories.
Guo Yun Wang - One of the best experts on this subject based on the ideXlab platform.
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Development and characterization of microsatellite markers for Chinese bayberry (Myrica rubra Sieb. & Zucc.).
Conservation Genetics, 2009Co-Authors: Shui Ming Zhang, Zhongshan Gao, Kunsong Chen, Guo Yun WangAbstract:Chinese bayberry (Myrica rubra Sieb. & Zucc.) is one of the most important Fruit Tree Crops in southern China. This paper reports the development of microsatellite primers for Chinese bayberry. By screening the expressed sequence tag database and other sources, 11 polymorphic loci were generated and primers were designed. Polymorphism of these 11 loci was assessed in 32 cultivars. All the 11 loci are polymorphic and the number of alleles (A) ranged from 3 to 12, observed heterozygosity (HO) and expected heterozygosity (HE) ranged from 0.1250 to 0.9667 and 0.2359 to 0.8790, respectively, polymorphism information content ranged from 0.2285 to 0.8516. These microsatellite loci should be useful in the studies of genetic diversity of Myrica rubra and will provide useful implications for resource conservation.
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Genetic Diversity of Chinese Bayberry (Myrica rubra Sieb. et Zucc.) Accessions Revealed by Amplified Fragment Length Polymorphism
HortScience, 2009Co-Authors: Shui Ming Zhang, Zhongshan Gao, Kunsong Chen, Guo Yun Wang, Jintu Zheng, Lu TingAbstract:Amplified fragment length polymorphism (AFLP) was used to analyze genetic diversity of 100 accessions of Chinese bayberry (Myrica rubra Sieb. et Zucc.), one of the widely cultivated Fruit Tree Crops in southern China. Six E-NN/M-NNN primer combinations were selected and a total of 236 bands were obtained, of which 177 were polymorphic (75.01%). An unweighted pair-group method of the arithmetic averages (UPGMA) was used to analyze the genetic relationships. The Dice's similarity coefficient among the Chinese bayberry accessions ranged from 0.75 to 1.00 and was 0.49 between Chinese bayberry and wax myrtle (M. cerifera L.). The 100 accessions of Chinese bayberry were clustered into two groups and seven subgroups. Subgrouping of Chinese bayberry was not related to the sex of the plant and color or size of the ripe Fruit, but to some extent the region where the accession originated. However, the accessions from the same region did not necessarily belong to the same group or subgroup, which suggested the presence of extensive gene flow among different regions. Furthermore, close relationships between some morphologically similar accessions were found.
Marco A. Peña - One of the best experts on this subject based on the ideXlab platform.
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Classifying Fruit-Tree Crops by Landsat-8 time series
2017 First IEEE International Symposium of Geoscience and Remote Sensing (GRSS-CHILE), 2017Co-Authors: Marco A. Peña, Alexander Brenning, Renfang LiaoAbstract:Landsat-8 time series were used to classify major Crops types in Maipo and Aconcagua valleys, central Chile. In the former valley four Fruit-Tree Crops were classified applying different machine learning techniques on feature sets comprising typical index-based temporal profiles, like those using the normalized difference vegetation index, and the complete spectral resolution of the time series. In the latter valley six Fruit-Tree Crops were classified only by LDA (linear discriminant analysis), found the best performing classifier for the Maipo Valley. LDA was applied on the complete spectral resolution of the time series and on a feature set adding all possible NDIs (normalized difference indices) that can be constructed from the time series. Regardless of the feature set used good MERs (misclassification error rates) were found (≤ 0.21) for the Maipo's Crops, but they were reduced by 4 and 13 percentage points, depending on the classifier and the training sample size used, when using the complete spectral resolution of the time series. We further explored these findings in the Aconcagua Valley, where MERs were reduced from 0.13 to 0.1 when the NDI-based feature set was used. In both study cases, the most predictive bands belonged to the first image dates of the time series, corresponding to the Crops' greenup stage, and they were placed not only on the typical greenness spectral region but also on the shortwave infrared region.
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Using spectrotemporal indices to improve the Fruit-Tree crop classification accuracy
ISPRS Journal of Photogrammetry and Remote Sensing, 2017Co-Authors: Marco A. Peña, Renfang Liao, Alexander BrenningAbstract:Abstract This study assesses the potential of spectrotemporal indices derived from satellite image time series (SITS) to improve the classification accuracy of Fruit-Tree Crops. Six major Fruit-Tree crop types in the Aconcagua Valley, Chile, were classified by applying various linear discriminant analysis (LDA) techniques on a Landsat-8 time series of nine images corresponding to the 2014–15 growing season. As features we not only used the complete spectral resolution of the SITS, but also all possible normalized difference indices (NDIs) that can be constructed from any two bands of the time series, a novel approach to derive features from SITS. Due to the high dimensionality of this “enhanced” feature set we used the lasso and ridge penalized variants of LDA (PLDA). Although classification accuracies yielded by the standard LDA applied on the full-band SITS were good (misclassification error rate, MER = 0.13), they were further improved by 23% (MER = 0.10) with ridge PLDA using the enhanced feature set. The most important bands to discriminate the Crops of interest were mainly concentrated on the first two image dates of the time series, corresponding to the Crops’ greenup stage. Despite the high predictor weights provided by the red and near infrared bands, typically used to construct greenness spectral indices, other spectral regions were also found important for the discrimination, such as the shortwave infrared band at 2.11–2.19 μm, sensitive to foliar water changes. These findings support the usefulness of spectrotemporal indices in the context of SITS-based crop type classifications, which until now have been mainly constructed by the arithmetic combination of two bands of the same image date in order to derive greenness temporal profiles like those from the normalized difference vegetation index.
Alon Bengal - One of the best experts on this subject based on the ideXlab platform.
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long term growth water consumption and yield of date palm as a function of salinity
Agricultural Water Management, 2011Co-Authors: Effi Tripler, Uri Shani, Yechezkel Mualem, Alon BengalAbstract:Actual measurements of water uptake and use, and the effect of water quality considerations on evapotranspiration (ET), are indispensable for understanding root zone processes and for the development of predictive plant growth models. The driving hypothesis of this research was that root zone stress response mechanisms in perennial Fruit Tree Crops is dynamic and dependent on Tree maturity and reproductive capability. This was tested by investigating long-term ET, biomass production and Fruit yield in date palms (Phoenix dactylifera L., cv. Medjool) under conditions of salinity. Elevated salinity levels in the soil solution were maintained for 6 years in large weighing-drainage lysimeters by irrigation with water having electrical conductivity (EC) of 1.8, 4, 8 and 12dSm−1. Salinity acted dynamically with a long-term consequence of increasing relative negative response to water consumption and plant growth that may be explained either as an accumulated effect or increasing sensitivity. Sensitivity to salinity stabilized at the highest measured levels after the Trees matured and began producing Fruit. Date palms were found to be much less tolerant to salinity than expected based on previous literature. Trees irrigated with low salinity (EC=1.8dSm−1) water were almost twice the size (based on ET and growth rates) than Trees irrigated with EC=4dSm−1 water after 5 years. Fruit production of the larger Trees was 35–50% greater than for the smaller, salt affected, Trees. Long term irrigation with very high EC of irrigation water (8 and 12dSm−1) was found to be commercially impractical as growth and yield were severely reduced. The results raise questions regarding the nature of mechanisms for salinity tolerance in date palms, indicate incentives to irrigate dates with higher rather than lower quality water, and present a particular challenge for modelers to correctly choose salinity response functions for dates as well as other perennial Crops.
Renfang Liao - One of the best experts on this subject based on the ideXlab platform.
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Classifying Fruit-Tree Crops by Landsat-8 time series
2017 First IEEE International Symposium of Geoscience and Remote Sensing (GRSS-CHILE), 2017Co-Authors: Marco A. Peña, Alexander Brenning, Renfang LiaoAbstract:Landsat-8 time series were used to classify major Crops types in Maipo and Aconcagua valleys, central Chile. In the former valley four Fruit-Tree Crops were classified applying different machine learning techniques on feature sets comprising typical index-based temporal profiles, like those using the normalized difference vegetation index, and the complete spectral resolution of the time series. In the latter valley six Fruit-Tree Crops were classified only by LDA (linear discriminant analysis), found the best performing classifier for the Maipo Valley. LDA was applied on the complete spectral resolution of the time series and on a feature set adding all possible NDIs (normalized difference indices) that can be constructed from the time series. Regardless of the feature set used good MERs (misclassification error rates) were found (≤ 0.21) for the Maipo's Crops, but they were reduced by 4 and 13 percentage points, depending on the classifier and the training sample size used, when using the complete spectral resolution of the time series. We further explored these findings in the Aconcagua Valley, where MERs were reduced from 0.13 to 0.1 when the NDI-based feature set was used. In both study cases, the most predictive bands belonged to the first image dates of the time series, corresponding to the Crops' greenup stage, and they were placed not only on the typical greenness spectral region but also on the shortwave infrared region.
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Using spectrotemporal indices to improve the Fruit-Tree crop classification accuracy
ISPRS Journal of Photogrammetry and Remote Sensing, 2017Co-Authors: Marco A. Peña, Renfang Liao, Alexander BrenningAbstract:Abstract This study assesses the potential of spectrotemporal indices derived from satellite image time series (SITS) to improve the classification accuracy of Fruit-Tree Crops. Six major Fruit-Tree crop types in the Aconcagua Valley, Chile, were classified by applying various linear discriminant analysis (LDA) techniques on a Landsat-8 time series of nine images corresponding to the 2014–15 growing season. As features we not only used the complete spectral resolution of the SITS, but also all possible normalized difference indices (NDIs) that can be constructed from any two bands of the time series, a novel approach to derive features from SITS. Due to the high dimensionality of this “enhanced” feature set we used the lasso and ridge penalized variants of LDA (PLDA). Although classification accuracies yielded by the standard LDA applied on the full-band SITS were good (misclassification error rate, MER = 0.13), they were further improved by 23% (MER = 0.10) with ridge PLDA using the enhanced feature set. The most important bands to discriminate the Crops of interest were mainly concentrated on the first two image dates of the time series, corresponding to the Crops’ greenup stage. Despite the high predictor weights provided by the red and near infrared bands, typically used to construct greenness spectral indices, other spectral regions were also found important for the discrimination, such as the shortwave infrared band at 2.11–2.19 μm, sensitive to foliar water changes. These findings support the usefulness of spectrotemporal indices in the context of SITS-based crop type classifications, which until now have been mainly constructed by the arithmetic combination of two bands of the same image date in order to derive greenness temporal profiles like those from the normalized difference vegetation index.
