Discovering and Generating Hard Examples for Training a Red Tide Detector

Currently, accurate detection of natural phenomena, such as red tide, that adversely affect wildlife and human, using satellite images has been increasingly utilized. However, red tide detection on satellite images still remains a very hard task due to unpredictable nature of red tide occurrence, extreme sparsity of red tide samples, difficulties in accurate groundtruthing, etc. In this paper, we aim to tackle both the data sparsity and groundtruthing issues by primarily addressing two challenges: i) significant lack of hard examples of non-red tide that can enhance detection performance and ii) extreme data imbalance between red tide and non-red tide examples. In the proposed work, we devise a 9-layer fully convolutional network jointly optimized with two plug-in modules tailored to overcoming the two challenges: i) a hard negative example generator (HNG) to supplement the hard negative (non-red tide) examples and ii) cascaded online hard example mining (cOHEM) to ease the data imbalance. Our proposed network jointly trained with HNG and cOHEM provides state-of-the-art red tide detection accuracy on GOCI satellite images.Comment: 10 page

Uncertainties in the Geostationary Ocean Color Imager (GOCI) Remote Sensing Reflectance for Assessing Diurnal Variability of Biogeochemical Processes

Short-term (sub-diurnal) biological and biogeochemical processes cannot be fully captured by the current suite of polar-orbiting satellite ocean color sensors, as their temporal resolution is limited to potentially one clear image per day. Geostationary sensors, such as the Geostationary Ocean Color Imager (GOCI) from the Republic of Korea, allow the study of these short-term processes because their orbit permit the collection of multiple images throughout each day for any area within the sensors field of regard. Assessing the capability to detect sub-diurnal changes in in-water properties caused by physical and biogeochemical processes characteristic of open ocean and coastal ocean ecosystems, however, requires an understanding of the uncertainties introduced by the instrument and/or geophysical retrieval algorithms. This work presents a study of the uncertainties during the daytime period for an ocean region with characteristically low-productivity with the assumption that only small and undetectable changes occur in the in-water properties due to biogeochemical processes during the daytime period. The complete GOCI mission data were processed using NASAs SeaDAS/l2gen package. The assumption of homogeneity of the study region was tested using three-day sequences and diurnal statistics. This assumption was found to hold based on the minimal diurnal and day-to-day variability in GOCI data products. Relative differences with respect to the midday value were calculated for each hourly observation of the day in order to investigate what time of the day the variability is greater. Also, the influence of the solar zenith angle in the retrieval of remote sensing reflectances and derived products was examined. Finally, we determined that the uncertainties in water-leaving remote-sensing reflectance (Rrs) for the 412,443, 490, 555, 660 and 680 nm bands on GOCI are 8.05 x 10(exp -4), 5.49 x 10(exp -4), 4.48 x 10(exp -4), 2.51 x 10(exp -4), 8.83 x 10(exp -5), and 1.36 x 10(exp -4)/sr, respectively, and 1.09 x 10(exp -2)/cu.mgm for the chlorophyll-a concentration (Chl-a), 2.09 x 10(exp -3)/m for the absorption coefficient of chromophoric dissolved organic matter at 412 nm (a(sub g) (412)), and 3.7 mg/cu.m for particulate organic carbon (POC). These R(sub rs) values can be considered the threshold values for detectable changes of the in-water properties due to biological, physical or biogeochemical processes from GOCI

Spatial distribution of Culex mosquito abundance and associated risk factors in Hanoi, Vietnam

Japanese encephalitis (JE) is the major cause of viral encephalitis (VE) in most AsianPacific countries. In Vietnam, there is no nationwide surveillance system for JE due to lack of medical facilities and diagnoses. Culex tritaeniorhynchus, Culex vishnui, and Culex quin-quefasciatus have been identified as the major JE vectors in Vietnam. The main objective of this study was to forecast a risk map of Culex mosquitoes in Hanoi, which is one of the most densely populated cities in Vietnam. A total of 10,775 female adult Culex mosquitoes were collected from 513 trapping locations. We collected temperature and precipitation information during the study period and its preceding month. In addition, the other predictor variables (e.g., normalized difference vegetation index [NDVI], land use/land cover and human population density), were collected for our analysis. The final model selected for estimating the Culex mosquito abundance included centered rainfall, quadratic term rainfall, rice cover ratio, forest cover ratio, and human population density variables. The estimated spatial distribution of Culex mosquito abundance ranged from 0 to more than 200 mosquitoes per 900m2. Our model estimated that 87% of the Hanoi area had an abundance of mosquitoes from 0 to 50, whereas approximately 1.2% of the area showed more than 150 mosquitoes, which was mostly in the rural/peri-urban districts. Our findings provide better insight into understanding the spatial distribution of Culex mosquitoes and its associated environmental risk factors. Such information can assist local clinicians and public health policymakers to identify potential areas of risk for JE virus. Risk maps can be an efficient way of raising public awareness about the virus and further preventive measures need to be considered in order to prevent outbreaks and onwards transmission of JE virus

Report on IOCCG Workshop Phytoplankton Composition from Space: towards a validation\ud strategy for satellite algorithms

The IOCCG-supported workshop “Phytoplankton Composition from Space: towards a validation strategy for satellite algorithms” was organized as a follow-up to the Phytoplankton Functional Types from Space splinter session, held at the International Ocean Colour Science Meeting (Germany, 2013). The specific goals of the workshop were to: 1. Provide a summary of the status of activities from relevant IOCCG working groups, the 2nd PFT intercomparison working group, PFT validation data sets and other research developments. 2. Provide a PFT validation strategy that considers the different applications of PFT products: and seeks community consensus on datasets and analysis protocols. 3. Discuss possibilities for sustaining ongoing PFT algorithm validation and intercomparison activities. The workshop included 15 talks, breakout sessions and plenary discussions. Talks covered community algorithm intercomparison activity updates, review of established and novel methods for PFT validation, validation activities for specific applications and space-agency requirements for PFT products and validation. These were followed by general discussions on (a) major recommendations for global intercomparison initiative in respect to validation, intercomparison and user’s guide; (b) developing a community consensus on which data sets for validation are optimal and which measurement and analysis protocols should be followed to support sustained validation of PFT products considering different applications; (c) the status of different validation data bases and measurement protocols for different PFT applications, and (d) engagement of the various user communities for PFT algorithms in developing PFT product specifications. From these discussions, two breakout groups provided in depth discussion and recommendations on (1) validation of current algorithms and (2) work plan to prepare for validation of future missions. Breakout group 1 provided an action list for progressing the current international community validation and intercomparison activity. Breakout group 2 provided the following recommendations towards developing a future validation strategy for satellite PFT products: 1. Establish a number of validation sites that maintain measurements of a key set of variables. 2. This set of variables should include: • Phytoplankton pigments from HPLC, phycobilins from spectrofluorometry • Phytoplankton cell counts and ID, volume / carbon estimation and imaging (e.g. from flow cytometry, FlowCam, FlowCytobot type technologies) • Inherent optical properties (e.g. absorption, backscattering, VSF) • Hyperspectral radiometry (both above and in-water) • Particle size distribution • Size-fractionated measurements of pigments and absorption • Genetic / -omics data 3. Undertake an intercomparison of methods / instruments over several years at a few sites to understand our capabilities to fully characterize the phytoplankton community. 4. Organise workshops to address the following topics: • Techniques for particle analysis, characterization and classification • Engagement with modellers and understanding end-user requirements • Data storage and management, standards for data contributors, data challenges In conclusion, the workshop was assessed to have fulfilled its goals. A follow-on meeting will be organized during the International Ocean Colour Science Meeting 2015 in San Francisco. Specific follow-on actions are listed at the end of the report

Manifold learning for robust classification of hyperspectral data

Accurate land cover classification that ensures robust mapping under diverse acquisition conditions is important in environmental studies where the identification of the land cover changes and its quantification have critical implications for management practices, functioning of ecosystems, and impact of climate. While remote sensing data have served as a useful tool for large scale monitoring of the earth, hyperspectral data offer an enhanced capability for more accurate land cover classification. However, constructing a robust classification framework for hyperspectral data poses issues that stem from inherent properties of hyperspectral data, including highly correlated spectral bands, high dimensionality of data, nonlinear spectral responses, and nonstationarity of samples in space and time. This dissertation addresses the issues in hyperspectral data classification by leveraging the concept of manifolds. A manifold is a nonlinear low dimensional subspace that is supported by data samples. Manifolds can be exploited in developing robust feature extraction and classification methods that are pertinent to the aforementioned issues. In this dissertation, various manifold learning algorithms that are widely used in machine learning community are investigated for the classification of hyperspectral data. Performance of global and local manifold learning methods is investigated in terms of (a) parameter values, (b) number of features retained, and (c) scene characteristics of hyperspectral data. The empirical study involving several data sets with diverse characteristics is outlined in Chapter 3. Results indicate that the manifold coordinates produce generally higher classification accuracies compared to those obtained by linear feature extraction methods, when they are used with proper settings. Chapter 4 addresses two limitations in manifold learning—(a) heavy computational requirements and (b) lack of attention to spatial context—which limits the applicability of manifold learning algorithms for large scale remote sensing data. Approximation approaches such as the Nyström methods are employed to mitigate the computation burden, where a set of landmark samples is first selected for the construction of the approximate manifolds, and the remaining samples are then linearly embedded in the manifold. While various landmark selection schemes are possible (e.g. random selection, clustering based approaches), spatially representative samples that are potentially relevant to data on grids can be obtained if the spatial context is considered in the selection scheme. A framework for representing the spatial coherence of samples is proposed using the kernel feature extraction framework. The proposed method produces a set of new features in which a unique spatial coherence pattern for homogeneous regions is captured in the individual features, which yield high classification accuracies and qualitatively superior results. Finally, an adaptive classification framework that exploits manifolds is proposed to obtain robust classification results for hyperspectral data. Spectral signatures can vary significantly across extended areas, often resulting in poor classification of land cover. The proposed adaptive framework employs a manifold regularization classifier, where the classifier is trained with labeled samples in one location and adapted to samples in spatially disjoint areas that exhibit significantly different distributions. In experimental studies, classification accuracies were higher for the proposed approach than for other kernel based semi-supervised classification methods

Morphological Band Registration of Multispectral Cameras for Water Quality Analysis with Unmanned Aerial Vehicle

Multispectral imagery contains abundant spectral information on terrestrial and oceanic targets, and retrieval of the geophysical variables of the targets is possible when the radiometric integrity of the data is secured. Multispectral cameras typically require the registration of individual band images because their lens locations for individual bands are often displaced from each other, thereby generating images of different viewing angles. Although this type of displacement can be corrected through a geometric transformation of the image coordinates, a mismatch or misregistration between the bands still remains, owing to the image acquisition timing that differs by bands. Even a short time difference is critical for the image quality of fast-moving targets, such as water surfaces, and this type of deformation cannot be compensated for with a geometric transformation between the bands. This study proposes a novel morphological band registration technique, based on the quantile matching method, for which the correspondence between the pixels of different bands is not sought by their geometric relationship, but by the radiometric distribution constructed in the vicinity of the pixel. In this study, a Micasense Rededge-M camera was operated on an unmanned aerial vehicle and multispectral images of coastal areas were acquired at various altitudes to examine the performance of the proposed method for different spatial scales. To assess the impact of the correction on a geophysical variable, the performance of the proposed method was evaluated for the chlorophyll-a concentration estimation. The results showed that the proposed method successfully removed the noisy spatial pattern caused by misregistration while maintaining the original spatial resolution for both homogeneous scenes and an episodic scene with a red tide outbreak

Analysis on the Fire Progression and Severity Variation of the Massive Forest Fire Occurred in Uljin, Korea, 2022

Analysis of the progression of forest fires is critical in understanding fire regimes and managing the risk of active fires. Major fire events in Korea mostly occur in the eastern mountainous areas (Gangwon Province), where the wind and moisture conditions are prone to fire in the late winter season. Despite the significance of the fire events in the area both in terms of frequency and severity, their spatial progression characteristics and their dependency on forest types have not been sufficiently analyzed so far, particularly with satellite data. This study first derived the severity map for the Uljin fire which occurred in March 2022, using a series of satellite images acquired over the fire period with very high frequency (every 5 days), and analyzed the characteristics of spatio-temporal progression in terms of forest types. The analysis revealed that the core fire area expanded very rapidly in the first few days, followed by an intensification phase that elevated severity in the active areas with marginal expansion in the peripheral areas. The analysis of the progression showed that the fire did not expand selectively by the forest type, despite the clear difference in their severity levels in the burned areas, where coniferous forest exhibited 3 times higher severity than deciduous forest

Analysis on the Fire Progression and Severity Variation of the Massive Forest Fire Occurred in Uljin, Korea, 2022

Analysis of the progression of forest fires is critical in understanding fire regimes and managing the risk of active fires. Major fire events in Korea mostly occur in the eastern mountainous areas (Gangwon Province), where the wind and moisture conditions are prone to fire in the late winter season. Despite the significance of the fire events in the area both in terms of frequency and severity, their spatial progression characteristics and their dependency on forest types have not been sufficiently analyzed so far, particularly with satellite data. This study first derived the severity map for the Uljin fire which occurred in March 2022, using a series of satellite images acquired over the fire period with very high frequency (every 5 days), and analyzed the characteristics of spatio-temporal progression in terms of forest types. The analysis revealed that the core fire area expanded very rapidly in the first few days, followed by an intensification phase that elevated severity in the active areas with marginal expansion in the peripheral areas. The analysis of the progression showed that the fire did not expand selectively by the forest type, despite the clear difference in their severity levels in the burned areas, where coniferous forest exhibited 3 times higher severity than deciduous forest

Development of Red Tide Detection Algorithm using GOCI Image based on Random Forest

The socio-economic damages on the fishery and aquacultural industries caused by the red tide have been increased in Korea. The remote sensing techniques using the ocean color (OC) satellite imagery has been developed in order to observe the red tide. However, the Korean red tide information system (RTIS) is still relying on ship surveillance. It has limitations to cover the whole coastal area as well as take lots of cost and time. This study developed the random forest (RF) based red tide detection model using the Geostationary Ocean Color Imager (GOCI) satellite imagery which has a higher spatio-temporal resolution (i.e., 500 x 500m, hourly). The spectral characteristics, quantitative and qualitative analysis, and spatio-temporal analysis of red tides in the South Sea of Korea during July ??? August 2018 were examined. The RF model showed promising detection accuracy (R2 = 0.701) than the other three algorithms at high concentrations (over 1,000 cells/mL) quantitatively as well as qualitatively. (i.e., modified red tide index (MRI, R2 = 0.192), red-to-blue ratio (RBR, R2 = 0.683), and spectral shape (SS, R2 = 0.531)). The detection model can provide an accurate red tide alert map in near-realtime as well as contribute to reducing socio-economic damages from the red tides in Korea