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Rice is a globally important crop that will continue to play an essential role in feeding our world as we grapple with climate change and population growth. Lodging is a primary threat to rice production, decreasing rice yield, and quality. Lodging assessment is a tedious task and requires heavy labor and a long duration due to the vast land areas involved. Newly developed autonomous crop scouting techniques have shown promise in mapping crop fields without any human interaction. By combining autonomous scouting and lodged rice detection with edge computing, it is possible to estimate rice lodging faster and at a much lower cost than previous methods. This study presents an adaptive crop scouting mechanism for Autonomous Unmanned Aerial Vehicles (UAV). We simulate UAV crop scouting of rice fields at multiple levels using deep neural networks and real UAV energy profiles, focusing on areas with high lodging. Using the proposed method, we can scout rice fields 36% faster than conventional scouting methods at 99.25% accuracy.
Ming-Der Yang; Jayson G. Boubin; Hui Ping Tsai; Hsin-Hung Tseng; Yu-Chun Hsu; Christopher C. Stewart. Adaptive autonomous UAV scouting for rice lodging assessment using edge computing with deep learning EDANet. Computers and Electronics in Agriculture 2020, 179, 105817 .
AMA StyleMing-Der Yang, Jayson G. Boubin, Hui Ping Tsai, Hsin-Hung Tseng, Yu-Chun Hsu, Christopher C. Stewart. Adaptive autonomous UAV scouting for rice lodging assessment using edge computing with deep learning EDANet. Computers and Electronics in Agriculture. 2020; 179 ():105817.
Chicago/Turabian StyleMing-Der Yang; Jayson G. Boubin; Hui Ping Tsai; Hsin-Hung Tseng; Yu-Chun Hsu; Christopher C. Stewart. 2020. "Adaptive autonomous UAV scouting for rice lodging assessment using edge computing with deep learning EDANet." Computers and Electronics in Agriculture 179, no. : 105817.
Water-soluble chemicals, involving a wide range of toxic chemicals in aqueous solutions, remain essential in both daily living or industrial uses. However, most toxicants are evaporated with water through their use and thus cause deleterious effects on the domestic environment and health in humans. Unfortunately, most current low-dose chemical vapor detection technologies are restricted by the use of sophisticated instruments and unable to promptly detect the quantity of diverse toxicants in a single analysis. To address these issues, this study reports the development of simple and fast chemical vapor detection using doctor-blade-coated macroporous poly(2-hydroxyethyl methacrylate)/poly(ethoxylated trimethylolpropane triacrylate) photonic crystals, in which the poly(2-hydroxyethyl methacrylate) has strong affinity to insecticide vapor owing to a favorable Gibbs free energy change for their mixing. The condensation of water-soluble chemical vapor therefore results in a significant reflection peak shift and an obvious color change. The visual colorimetric readout can be further improved by increasing the lattice spacing of the macroporous photonic crystals. Furthermore, the dependence of the reflection peak position on vapor pressure under actual conditions and the reproducibility of vapor detecting are also evaluated in this study.
Min-Fang Wu; Hui-Ping Tsai; Chia-Hua Hsieh; Yi-Cheng Lu; Liang-Cheng Pan; Hongta Yang. Water-Soluble Chemical Vapor Detection Enabled by Doctor-Blade-Coated Macroporous Photonic Crystals. Sensors 2020, 20, 5503 .
AMA StyleMin-Fang Wu, Hui-Ping Tsai, Chia-Hua Hsieh, Yi-Cheng Lu, Liang-Cheng Pan, Hongta Yang. Water-Soluble Chemical Vapor Detection Enabled by Doctor-Blade-Coated Macroporous Photonic Crystals. Sensors. 2020; 20 (19):5503.
Chicago/Turabian StyleMin-Fang Wu; Hui-Ping Tsai; Chia-Hua Hsieh; Yi-Cheng Lu; Liang-Cheng Pan; Hongta Yang. 2020. "Water-Soluble Chemical Vapor Detection Enabled by Doctor-Blade-Coated Macroporous Photonic Crystals." Sensors 20, no. 19: 5503.
The critical issue facing hyperspectral image (HSI) classification is the imbalance between dimensionality and the number of available training samples. This study attempted to solve the issue by proposing an integrating method using minimum noise fractions (MNF) and Hilbert–Huang transform (HHT) transformations into artificial neural networks (ANNs) for HSI classification tasks. MNF and HHT function as a feature extractor and image decomposer, respectively, to minimize influences of noises and dimensionality and to maximize training sample efficiency. Experimental results using two benchmark datasets, Indian Pine (IP) and Pavia University (PaviaU) hyperspectral images, are presented. With the intention of optimizing the number of essential neurons and training samples in the ANN, 1 to 1000 neurons and four proportions of training sample were tested, and the associated classification accuracies were evaluated. For the IP dataset, the results showed a remarkable classification accuracy of 99.81% with a 30% training sample from the MNF1–14+HHT-transformed image set using 500 neurons. Additionally, a high accuracy of 97.62% using only a 5% training sample was achieved for the MNF1–14+HHT-transformed images. For the PaviaU dataset, the highest classification accuracy was 98.70% with a 30% training sample from the MNF1–14+HHT-transformed image using 800 neurons. In general, the accuracy increased as the neurons increased, and as the training samples increased. However, the accuracy improvement curve became relatively flat when more than 200 neurons were used, which revealed that using more discriminative information from transformed images can reduce the number of neurons needed to adequately describe the data as well as reducing the complexity of the ANN model. Overall, the proposed method opens new avenues in the use of MNF and HHT transformations for HSI classification with outstanding accuracy performance using an ANN.
Ming-Der Yang; Kai-Hsiang Huang; Hui-Ping Tsai. Integrating MNF and HHT Transformations into Artificial Neural Networks for Hyperspectral Image Classification. Remote Sensing 2020, 12, 2327 .
AMA StyleMing-Der Yang, Kai-Hsiang Huang, Hui-Ping Tsai. Integrating MNF and HHT Transformations into Artificial Neural Networks for Hyperspectral Image Classification. Remote Sensing. 2020; 12 (14):2327.
Chicago/Turabian StyleMing-Der Yang; Kai-Hsiang Huang; Hui-Ping Tsai. 2020. "Integrating MNF and HHT Transformations into Artificial Neural Networks for Hyperspectral Image Classification." Remote Sensing 12, no. 14: 2327.
A rapid and precise large-scale agricultural disaster survey is a basis for agricultural disaster relief and insurance but is labor-intensive and time-consuming. This study applies Unmanned Aerial Vehicles (UAVs) images through deep-learning image processing to estimate the rice lodging in paddies over a large area. This study establishes an image semantic segmentation model employing two neural network architectures, FCN-AlexNet, and SegNet, whose effects are explored in the interpretation of various object sizes and computation efficiency. Commercial UAVs imaging rice paddies in high-resolution visible images are used to calculate three vegetation indicators to improve the applicability of visible images. The proposed model was trained and tested on a set of UAV images in 2017 and was validated on a set of UAV images in 2019. For the identification of rice lodging on the 2017 UAV images, the F1-score reaches 0.80 and 0.79 for FCN-AlexNet and SegNet, respectively. The F1-score of FCN-AlexNet using RGB + ExGR combination also reaches 0.78 in the 2019 images for validation. The proposed model adopting semantic segmentation networks is proven to have better efficiency, approximately 10 to 15 times faster, and a lower misinterpretation rate than that of the maximum likelihood method.
Ming-Der Yang; Hsin-Hung Tseng; Yu-Chun Hsu; Hui Ping Tsai. Semantic Segmentation Using Deep Learning with Vegetation Indices for Rice Lodging Identification in Multi-date UAV Visible Images. Remote Sensing 2020, 12, 633 .
AMA StyleMing-Der Yang, Hsin-Hung Tseng, Yu-Chun Hsu, Hui Ping Tsai. Semantic Segmentation Using Deep Learning with Vegetation Indices for Rice Lodging Identification in Multi-date UAV Visible Images. Remote Sensing. 2020; 12 (4):633.
Chicago/Turabian StyleMing-Der Yang; Hsin-Hung Tseng; Yu-Chun Hsu; Hui Ping Tsai. 2020. "Semantic Segmentation Using Deep Learning with Vegetation Indices for Rice Lodging Identification in Multi-date UAV Visible Images." Remote Sensing 12, no. 4: 633.
Ming-Der Yang; Hui Ping Tsai. Post-earthquake spatio-temporal landslide analysis of Huisun Experimental Forest Station. Terrestrial, Atmospheric and Oceanic Sciences 2019, 30, 493 -508.
AMA StyleMing-Der Yang, Hui Ping Tsai. Post-earthquake spatio-temporal landslide analysis of Huisun Experimental Forest Station. Terrestrial, Atmospheric and Oceanic Sciences. 2019; 30 (4):493-508.
Chicago/Turabian StyleMing-Der Yang; Hui Ping Tsai. 2019. "Post-earthquake spatio-temporal landslide analysis of Huisun Experimental Forest Station." Terrestrial, Atmospheric and Oceanic Sciences 30, no. 4: 493-508.
Oyster racks identification relies on manual in situ assessment and often leads to a compensation dispute in aquacultural damage assessment. This study proposes an efficient classification method to identify oyster racks using unmanned aerial vehiclesw (UAVs) images. After image preprocessed by the morphology-based opening operation and mosaicing, the Canny edge detection algorithm, an edge line reparation algorithm, and the multiresolution segmentation techniques are applied to recognize the boundary of oyster racks and the number of oyster racks are further obtained. In this study, a case study was carried out in the District 23 of Dongshi fishing port on September 4, 2015 and October 5, 2015 to evaluate the influence of the Typhoon Dujuan. Based on the image identification, eight horizontal racks were destroyed by the storm surges and strong winds, but the raft-string racks were increased by 60, which were steered from neighbor districts. The damaged ratio of oyster racks was successfully identified and 38 racks were eligible for disaster relief. Comparing to the current manual in situ damage assessment process taking over one month, the proposed process provides a timely and quantitative assessment by efficiently identifying oyster racks on the UAV images within one week, which demonstrated that the proposed process is a promising way for the efficient damage assessment of oyster racks.
Ming-Der Yang; Kai-Siang Huang; Jin Wan; Hui Ping Tsai; Liang-Mao Lin. Timely and Quantitative Damage Assessment of Oyster Racks Using UAV Images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2018, 11, 2862 -2868.
AMA StyleMing-Der Yang, Kai-Siang Huang, Jin Wan, Hui Ping Tsai, Liang-Mao Lin. Timely and Quantitative Damage Assessment of Oyster Racks Using UAV Images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2018; 11 (8):2862-2868.
Chicago/Turabian StyleMing-Der Yang; Kai-Siang Huang; Jin Wan; Hui Ping Tsai; Liang-Mao Lin. 2018. "Timely and Quantitative Damage Assessment of Oyster Racks Using UAV Images." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 11, no. 8: 2862-2868.
Vegetation recovery monitoring is critical for assessing denudation areas after landslides have occurred. A long-term and broad area investigation using remote sensing techniques is an efficient and cost-effective approach incorporating the consideration of radiometric correction and seasonality variations across multi-date satellite images. This paper investigates long-term vegetation recovery using 14 SPOT satellite images spanning from 1999 to 2011 over the landslide area of Mt. Jou-Jou in central Taiwan, which was caused by the Chi-Chi earthquake in 1999. The vegetation status was evaluated by the Normalized Difference Vegetation Index (NDVI) with radiometric correction between multi-date images based on pseudoinvariant features, and subsequently a vegetation recovery rate (VRR) model was empirically established after seasonality adjustment was performed on the multi-date NDVI images. An increasing tendency of the vegetation recovery in the landslide area of Mt. Jou-Jou appeared based on the NDVI value rising to 0.367 in March 2011 from −0.044 right after the catastrophic earthquake. The vegetation recovery rate with seasonality adjustment approached 81.5% for the total area and 81.3% for the landslide area through 12 years succession. The seasonality adjustment also enhanced the VRR model with a determination coefficient that increased from 0.883 to 0.916 for the landslide area and from 0.584 to 0.915 for the total area, highlighting the necessity of seasonality adjustment in multi-date vegetation observations using satellite images. Furthermore, the association between precipitation and NDVI was discussed, and the inverse relationship with the reoccurrence of high-intensity short-duration rainfall and yearly heavy rainfall was observed, in agreement with the on-site investigation.
Ming-Der Yang; Su-Chin Chen; Hui Ping Tsai. A Long-Term Vegetation Recovery Estimation for Mt. Jou-Jou Using Multi-Date SPOT 1, 2, and 4 Images. Remote Sensing 2017, 9, 893 .
AMA StyleMing-Der Yang, Su-Chin Chen, Hui Ping Tsai. A Long-Term Vegetation Recovery Estimation for Mt. Jou-Jou Using Multi-Date SPOT 1, 2, and 4 Images. Remote Sensing. 2017; 9 (9):893.
Chicago/Turabian StyleMing-Der Yang; Su-Chin Chen; Hui Ping Tsai. 2017. "A Long-Term Vegetation Recovery Estimation for Mt. Jou-Jou Using Multi-Date SPOT 1, 2, and 4 Images." Remote Sensing 9, no. 9: 893.
Surfactants are extensively used as detergents, dispersants, as well as emulsifiers. Thus, wastewater containing high-concentration surfactants discharged to the environment poses a serious threat to the ecosystem. Unfortunately, conventional detection methods for surfactants suffer from the use of sophisticated instruments, and cannot perform detections for various surfactants by a single analysis. The article reports the development of simple and sensitive surfactant detection using doctor blade coated three-dimensional curved macroporous photonic crystals on a cylindrical rod. The photonic crystals exhibit different hydrophobicities at various angular positions after surface modification. The penetration of aqueous surfactant solutions in the interconnected macropores causes red-shift as well as reduction in amplitude in the optical stop bands, resulting in surfactant detection with visible readout. The correlation between the surface tension as well as the solution-infiltrated angular position, and the concentration of aqueous surfactant solutions have also been investigated in this study.
Chien-Fu Chiu; Hui Ping Tsai; Ying-Chu Chen; Yi-Xuan He; Kun-Yi Andrew Lin; Hongta Yang. Self-Assembled Curved Macroporous Photonic Crystal-Based Surfactant Detectors. ACS Applied Materials & Interfaces 2017, 9, 26333 -26340.
AMA StyleChien-Fu Chiu, Hui Ping Tsai, Ying-Chu Chen, Yi-Xuan He, Kun-Yi Andrew Lin, Hongta Yang. Self-Assembled Curved Macroporous Photonic Crystal-Based Surfactant Detectors. ACS Applied Materials & Interfaces. 2017; 9 (31):26333-26340.
Chicago/Turabian StyleChien-Fu Chiu; Hui Ping Tsai; Ying-Chu Chen; Yi-Xuan He; Kun-Yi Andrew Lin; Hongta Yang. 2017. "Self-Assembled Curved Macroporous Photonic Crystal-Based Surfactant Detectors." ACS Applied Materials & Interfaces 9, no. 31: 26333-26340.
Because of heavy rains or earthquakes, landslide and debris flows may flow into rivers and block river channel so to become a landslide dam. Due to the relatively loose nature and the absence of proper engineering spillway, landslide dams are often vulnerable to catastrophic failure by overtopping and breaching, and cause severe flooding in downstream area possibly with serious casualties, such as life loss and property damage. Therefore, the technology development of time-effective monitoring landslide dams becomes an important issue in disaster mitigation. However, the monitoring and assessment of landslide is often difficult due to the timeliness and effectiveness over an unreachable location. Since the timing of dam failure and the magnitude of the resulting flooding downstream are highly affected by the landslide dam size and geometry, the three-dimensional measurement of landslide dam is an essential task in the mitigation act following the occurrence of a landslide lake. Taking responsiveness and efficiency into consideration, this research proposed a process for the monitoring and assessment of landslide dam by employing unmanned aerial vehicles (UAVs) for aerial surveying and Image based modeling (IBM) for aerial surveying and a 3D model reconstruction of an artificial landslide dam built in a real Landou stream within Hui-Sun experimental forest in Nantou, Taiwan, as a full-scale on-site experiment. The result shows that a 3D terrain model can be successfully constructed based on the UAV images through IBM process. Especially, the volume and stability of landslide dam is the most urgent information needed to be acquired for mitigation strategy making so that the failure of landslide dams due to dam breach and erosion by the overflow stream can be predicted and prepared.
Ming-Der Yang; Kai-Siang Huang; Hui-Ping Tsai. Monitoring and Measurement of an Artificial Landslide Dam Using UAV Images and Image-Based Modeling. DEStech Transactions on Computer Science and Engineering 2017, 1 .
AMA StyleMing-Der Yang, Kai-Siang Huang, Hui-Ping Tsai. Monitoring and Measurement of an Artificial Landslide Dam Using UAV Images and Image-Based Modeling. DEStech Transactions on Computer Science and Engineering. 2017; (mcsse):1.
Chicago/Turabian StyleMing-Der Yang; Kai-Siang Huang; Hui-Ping Tsai. 2017. "Monitoring and Measurement of an Artificial Landslide Dam Using UAV Images and Image-Based Modeling." DEStech Transactions on Computer Science and Engineering , no. mcsse: 1.
Rice lodging identification relies on manual in situ assessment and often leads to a compensation dispute in agricultural disaster assessment. Therefore, this study proposes a comprehensive and efficient classification technique for agricultural lands that entails using unmanned aerial vehicle (UAV) imagery. In addition to spectral information, digital surface model (DSM) and texture information of the images was obtained through image-based modeling and texture analysis. Moreover, single feature probability (SFP) values were computed to evaluate the contribution of spectral and spatial hybrid image information to classification accuracy. The SFP results revealed that texture information was beneficial for the classification of rice and water, DSM information was valuable for lodging and tree classification, and the combination of texture and DSM information was helpful in distinguishing between artificial surface and bare land. Furthermore, a decision tree classification model incorporating SFP values yielded optimal results, with an accuracy of 96.17% and a Kappa value of 0.941, compared with that of a maximum likelihood classification model (90.76%). The rice lodging ratio in paddies at the study site was successfully identified, with three paddies being eligible for disaster relief. The study demonstrated that the proposed spatial and spectral hybrid image classification technology is a promising tool for rice lodging assessment.
Ming-Der Yang; Kai-Siang Huang; Yi-Hsuan Kuo; Hui Ping Tsai; Liang-Mao Lin. Spatial and Spectral Hybrid Image Classification for Rice Lodging Assessment through UAV Imagery. Remote Sensing 2017, 9, 583 .
AMA StyleMing-Der Yang, Kai-Siang Huang, Yi-Hsuan Kuo, Hui Ping Tsai, Liang-Mao Lin. Spatial and Spectral Hybrid Image Classification for Rice Lodging Assessment through UAV Imagery. Remote Sensing. 2017; 9 (6):583.
Chicago/Turabian StyleMing-Der Yang; Kai-Siang Huang; Yi-Hsuan Kuo; Hui Ping Tsai; Liang-Mao Lin. 2017. "Spatial and Spectral Hybrid Image Classification for Rice Lodging Assessment through UAV Imagery." Remote Sensing 9, no. 6: 583.
This research reports the development of sensitive and reversible vapor detection by using three-dimensional macroporous photonic crystals. A scalable and roll-to-roll compatible doctor blade coating technology is utilized to fabricate flexible macroporous poly(ethoxylated trimethylolpropane triacrylate) (PETPTA) films with hexagonal close-packed pores which are interconnected. The pores are then coated with a layer of poly(2-hydroxyethyl methacrylate) (PHEMA) to create macroporous PHEMA/PETPTA films. The condensation of vapors in the PHEMA coated macroporous films leads to the increase of both the PHEMA swelling degree and the effective refractive index of the diffractive medium, resulting in the red-shift and amplitude reduction of the optical stop bands. The optical measurements reveal that the diffraction from the as-prepared macroporous photonic crystals sensitively monitors the vapor pressure of ethanol since the PHEMA layer displays a great volume dependence on ethanol due to a decreased Flory-Huggins mixing parameter. The dependence of the diffraction wavelength on vapor pressure and the reproducibility of vapor sensing have also been investigated in this study.
Ya-Ling Ko; Hui-Ping Tsai; Kun-Yi Lin; Ying-Chu Chen; Hongta Yang. Reusable macroporous photonic crystal-based ethanol vapor detectors by doctor blade coating. Journal of Colloid and Interface Science 2017, 487, 360 -369.
AMA StyleYa-Ling Ko, Hui-Ping Tsai, Kun-Yi Lin, Ying-Chu Chen, Hongta Yang. Reusable macroporous photonic crystal-based ethanol vapor detectors by doctor blade coating. Journal of Colloid and Interface Science. 2017; 487 ():360-369.
Chicago/Turabian StyleYa-Ling Ko; Hui-Ping Tsai; Kun-Yi Lin; Ying-Chu Chen; Hongta Yang. 2017. "Reusable macroporous photonic crystal-based ethanol vapor detectors by doctor blade coating." Journal of Colloid and Interface Science 487, no. : 360-369.
A non-lithography-based approach is developed in this study for assembling monolayer close-packed hemispherical nano-dimple arrays on both sides of a PET film by a scalable Langmuir-Blodgett technology. The resulting gratings greatly suppress specular reflection and therefore enhance specular transmission for a broad range of visible wavelengths, resulting from a gradual change in the effective refractive index at air/PET interface. The experimental results reveal that the antireflection properties of the as-fabricated coatings are affected by the size of the nano-dimples. Moreover, both optical performances of single-sided and dual-sided nano-dimple-structured coatings have been investigated in this study.
Cheng-Yen Lin; Kun-Yi Lin; Hui-Ping Tsai; Yi-Xuan He; Hongta Yang. Self-assembled dual-sided hemispherical nano-dimple-structured broadband antireflection coatings. Applied Physics Letters 2016, 109, 221601 .
AMA StyleCheng-Yen Lin, Kun-Yi Lin, Hui-Ping Tsai, Yi-Xuan He, Hongta Yang. Self-assembled dual-sided hemispherical nano-dimple-structured broadband antireflection coatings. Applied Physics Letters. 2016; 109 (22):221601.
Chicago/Turabian StyleCheng-Yen Lin; Kun-Yi Lin; Hui-Ping Tsai; Yi-Xuan He; Hongta Yang. 2016. "Self-assembled dual-sided hemispherical nano-dimple-structured broadband antireflection coatings." Applied Physics Letters 109, no. 22: 221601.
This study reports a self-assembly technology for fabricating retroreflection coatings with hierarchical nano-/micro-structures, which are inspired by the binary periodic structures found on the compound eyes of insect. Silica colloidal crystals of adjustable thicknesses are assembled on encountering glass microbeads by a Langmuir-Blodgett-like approach in a layer-by-layer manner. The as-assembled hierarchical structures exhibit a brilliant color caused by Bragg diffraction from the crystalline lattice of silica colloidal crystals on glass microbeads. The resultant coating is capable to reflect light in the opposite direction to the incident light. Moreover, the dependence of the silica particle size, colloidal crystal thickness, and incident angle on the retroreflective properties is investigated in this study.
Kuan-Yi Tsao; Hui-Ping Tsai; Kun-Yi Andrew Lin; Yi-Xuan He; Hongta Yang. Self-Assembled Hierarchical Arrays for Colored Retroreflective Coatings. Langmuir 2016, 32, 12869 -12875.
AMA StyleKuan-Yi Tsao, Hui-Ping Tsai, Kun-Yi Andrew Lin, Yi-Xuan He, Hongta Yang. Self-Assembled Hierarchical Arrays for Colored Retroreflective Coatings. Langmuir. 2016; 32 (48):12869-12875.
Chicago/Turabian StyleKuan-Yi Tsao; Hui-Ping Tsai; Kun-Yi Andrew Lin; Yi-Xuan He; Hongta Yang. 2016. "Self-Assembled Hierarchical Arrays for Colored Retroreflective Coatings." Langmuir 32, no. 48: 12869-12875.
The scattered pixel problem in hyperspectral images caused by atmospheric noises and incomplete classification can lead to unsatisfactory classification; this problem remains to be solved. This letter reports the application of minimum noise fractions (MNFs) combined with fast and adaptive bidimensional empirical mode decomposition (FABEMD) as a two-step process to improve the classification accuracy of airborne visible-infrared imaging spectrometer hyperspectral image of the Indian Pine data set. With dimensional reduction by using MNF, FABEMD, considered as a low-pass filter, decomposes a hyperspectral image into several bidimensional intrinsic mode functions (BIMFs) and a residue image. The first four BIMFs are removed and the remainder BIMFs are integrated to reconstruct informative images that are subsequently classified through a support vector machine classifier (SVM). The classification results show that the proposed approach can effectively eliminate noise effects and can obtain higher accuracy than does traditional MNF SVM.
Ming-Der Yang; Kai-Shiang Huang; Yeh Fen Yang; Liang-You Lu; Zheng-Yi Feng; Hui Ping Tsai. Hyperspectral Image Classification Using Fast and Adaptive Bidimensional Empirical Mode Decomposition With Minimum Noise Fraction. IEEE Geoscience and Remote Sensing Letters 2016, 13, 1950 -1954.
AMA StyleMing-Der Yang, Kai-Shiang Huang, Yeh Fen Yang, Liang-You Lu, Zheng-Yi Feng, Hui Ping Tsai. Hyperspectral Image Classification Using Fast and Adaptive Bidimensional Empirical Mode Decomposition With Minimum Noise Fraction. IEEE Geoscience and Remote Sensing Letters. 2016; 13 (12):1950-1954.
Chicago/Turabian StyleMing-Der Yang; Kai-Shiang Huang; Yeh Fen Yang; Liang-You Lu; Zheng-Yi Feng; Hui Ping Tsai. 2016. "Hyperspectral Image Classification Using Fast and Adaptive Bidimensional Empirical Mode Decomposition With Minimum Noise Fraction." IEEE Geoscience and Remote Sensing Letters 13, no. 12: 1950-1954.
Due to 4000 m elevation variation with temperature differences equivalent to 50 degrees of latitudinal gradient, exploring Taiwan’s spatial vegetation trends is valuable in terms of diverse ecosystems and climatic types covering a relatively small island with an area of 36,000 km2. This study analyzed Taiwan’s spatial vegetation trends with controlling environmental variables through redundancy (RDA) and hierarchical cluster (HCA) analyses over three decades (1982–2012) of monthly normalized difference vegetation index (NDVI) derived from the Advanced Very High Resolution Radiometer (AVHRR) NDVI3g data for 19 selected weather stations over the island. Results showed two spatially distinct vegetation response groups. Group 1 comprises weather stations which remained relatively natural showing a slight increasing NDVI tendency accompanied with rising temperature, whereas Group 2 comprises stations with high level of human development showing a slight decreasing NDVI tendency associated with increasing temperature-induced moisture stress. Statistically significant controlling variables include climatic factors (temperature and precipitation), orographic factors (mean slope and aspects), and anthropogenic factor (population density). Given the potential trajectories for future warming, variable precipitation, and population pressure, challenges, such as land-cover and water-induced vegetation stress, need to be considered simultaneously for establishing adequate adaptation strategies to combat climate change challenges in Taiwan.
Hui Ping Tsai; Yu-Hao Lin; Ming-Der Yang. Exploring Long Term Spatial Vegetation Trends in Taiwan from AVHRR NDVI3g Dataset Using RDA and HCA Analyses. Remote Sensing 2016, 8, 290 .
AMA StyleHui Ping Tsai, Yu-Hao Lin, Ming-Der Yang. Exploring Long Term Spatial Vegetation Trends in Taiwan from AVHRR NDVI3g Dataset Using RDA and HCA Analyses. Remote Sensing. 2016; 8 (4):290.
Chicago/Turabian StyleHui Ping Tsai; Yu-Hao Lin; Ming-Der Yang. 2016. "Exploring Long Term Spatial Vegetation Trends in Taiwan from AVHRR NDVI3g Dataset Using RDA and HCA Analyses." Remote Sensing 8, no. 4: 290.
This research aims to improve our understanding of vegetation dynamics and associated climate variables in Taiwan by utilizing mean-variance analysis (MVA), relative directional persistence analysis, and Pearson's product moment correlation analysis on the Advanced Very High Resolution Radiometer (AVHRR)-derived NDVI3g data from 1982 to 2012. The results indicate a slightly increasing mean-normalized difference vegetation index (NDVI) value with a relatively higher variance during the 1990s and lower variance during the 2000s, which may be explained by the observed fluctuation in precipitation. Additionally, NDVI patterns are identified as increasing in the first half of the year and decreasing in the second half of the year. Spatially, decreasing patterns are observed in all regions except that the northern counties exhibit an increasing NDVI pattern supported by the observed increase in precipitation. Moreover, sunshine duration and temperature are positively correlated with NDVI, whereas precipitation and cloud amount exhibit a negative correlation with NDVI in Taiwan. In the context of global environmental change, this research highlights the utility of applying a combined spatial-temporal approach to remote sensing products. This is an approach with potential applications such as landscape management, conservation practice, and water resource management for policy makers and stakeholders in and beyond Taiwan.
Hui Ping Tsai; Ming-Der Yang. Relating Vegetation Dynamics to Climate Variables in Taiwan Using 1982–2012 NDVI3g Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2016, 9, 1624 -1639.
AMA StyleHui Ping Tsai, Ming-Der Yang. Relating Vegetation Dynamics to Climate Variables in Taiwan Using 1982–2012 NDVI3g Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2016; 9 (4):1624-1639.
Chicago/Turabian StyleHui Ping Tsai; Ming-Der Yang. 2016. "Relating Vegetation Dynamics to Climate Variables in Taiwan Using 1982–2012 NDVI3g Data." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 9, no. 4: 1624-1639.
Despite the existence of long term remotely sensed datasets, change detection methods are limited and often remain an obstacle to the effective use of time series approaches in remote sensing applications to Land Change Science. This paper establishes some simple statistical tests to be applied to NDVI-derived time series of remotely sensed data products. Specifically, the methods determine the statistical significance of three separate metrics of the persistence of vegetation cover or changes within a landscape by comparison to various forms of “benchmarks”; directional persistence (changes in sign relative to some fixed reference value), relative directional persistence (changes in sign relative to the preceding value), and massive persistence (changes in magnitude relative to the preceding value). Null hypotheses are developed on the basis of serially independent, normally distributed random variables. Critical values are established theoretically through consideration of the numeric properties of those variables, application of extensive Monte Carlo simulations, and parallels to random walk processes. Monthly pixel-level NDVI values for the state of Florida are analyzed over 25 years, illustrating the techniques’ abilities to identify areas and/or times of significant change, and facilitate a more detailed understanding of this landscape. The potential power and utility of such techniques is diverse within the area of remote sensing studies and Land Change Science, especially in the context of global change.
Peter Waylen; Jane Southworth; Cerian Gibbes; Huiping Tsai. Time Series Analysis of Land Cover Change: Developing Statistical Tools to Determine Significance of Land Cover Changes in Persistence Analyses. Remote Sensing 2014, 6, 4473 -4497.
AMA StylePeter Waylen, Jane Southworth, Cerian Gibbes, Huiping Tsai. Time Series Analysis of Land Cover Change: Developing Statistical Tools to Determine Significance of Land Cover Changes in Persistence Analyses. Remote Sensing. 2014; 6 (5):4473-4497.
Chicago/Turabian StylePeter Waylen; Jane Southworth; Cerian Gibbes; Huiping Tsai. 2014. "Time Series Analysis of Land Cover Change: Developing Statistical Tools to Determine Significance of Land Cover Changes in Persistence Analyses." Remote Sensing 6, no. 5: 4473-4497.
The study analyzes the spatial persistence and temporal patterns in vegetation cover across Florida by utilizing the Normalized Difference Vegetation Index (NDVI) time-series data derived from the Advanced Very High Resolution Radiometer from 1982 to 2006. Specifically, mean-variance analysis and persistence metrics are used to discern the significance of vegetation patterns, the significance of land cover and land-use change, and the relevance of climate variability across time and space. Results demonstrate a consistent, increasing pattern in the mean NDVI and its variance, especially during the late fall and winter season. A possible explanation of this increasing pattern is based on the Atlantic Multidecadal Oscillation, which switched from cold to warm phase after 1995 and is associated with increased winter precipitation. Additionally, the impacts of the El Niño Southern Oscillation can be detected through the decreased spatial variances of NDVI in warm-phase events, compared to cold-phase events, and the more pronounced nature of the pattern in fall/winter. This study proposes a novel set of techniques applied to satellite-derived vegetation data, which effectively discerns fine, statewide vegetation dynamics at appropriate spatial and temporal scales.
Huiping Tsai; Jane Southworth; Peter Waylen. Spatial persistence and temporal patterns in vegetation cover across Florida, 1982–2006. Physical Geography 2014, 35, 151 -180.
AMA StyleHuiping Tsai, Jane Southworth, Peter Waylen. Spatial persistence and temporal patterns in vegetation cover across Florida, 1982–2006. Physical Geography. 2014; 35 (2):151-180.
Chicago/Turabian StyleHuiping Tsai; Jane Southworth; Peter Waylen. 2014. "Spatial persistence and temporal patterns in vegetation cover across Florida, 1982–2006." Physical Geography 35, no. 2: 151-180.