Detecting trend and seasonal changes in bathymetry derived from HICO imagery: A case study of Shark Bay, Western Australia

The Hyperspectral Imager for the Coastal Ocean (HICO) aboard the International Space Station has offered for the first time a dedicated space-borne hyperspectral sensor specifically designed for remote sensing of the coastal environment. However, several processing steps are required to convert calibrated top-of-atmosphere radiances to the desired geophysical parameter(s). These steps add various amounts of uncertainty that can cumulatively render the geophysical parameter imprecise and potentially unusable if the objective is to analyze trends and/or seasonal variability. This research presented here has focused on: (1) atmospheric correction of HICO imagery; (2) retrieval of bathymetry using an improved implementation of a shallow water inversion algorithm; (3) propagation of uncertainty due to environmental noise through the bathymetry retrieval process; (4) issues relating to consistent geo-location of HICO imagery necessary for time series analysis, and; (5) tide height corrections of the retrieved bathymetric dataset. The underlying question of whether a temporal change in depth is detectable above uncertainty is also addressed. To this end, nine HICO images spanning November 2011 to August 2012, over the Shark Bay World Heritage Area, Western Australia, were examined. The results presented indicate that precision of the bathymetric retrievals is dependent on the shallow water inversion algorithm used. Within this study, an average of 70% of pixels for the entire HICO-derived bathymetry dataset achieved a relative uncertainty of less than ± 20%. A per-pixel t-test analysis between derived bathymetry images at successive timestamps revealed observable changes in depth to as low as 0.4 m. However, the present geolocation accuracy of HICO is relatively poor and needs further improvements before extensive time series analysis can be performed

Analysing Threshold Value in Fire Detection Algorithm Using MODIS Data

Abstract - MODIS instruments have been designed to include special channels for fire monitoring by adding more spectral thermal band detector on them. The basic understanding of remote sensing fire detection should be kept in mind to be able to improve the algorithm for regional scale detection purposes. It still gives many chances for more exploration. This paper describe the principle of fire investigation applied on MODIS data. The main used algorithm in this research is contextual algorithm which has been developed by NASA scientist team. By varying applied threshold of T4 value in the range of 320-360K it shows that detected fire is significantly changed. While significant difference of detected FHS by changing ΔT threshold value is occurred in the range of 15-35K. Improve and adjustment of fire detection algorithm is needed to get the best accuracy result proper to local or regional conditions. MOD14 algorithm is applied threshold values of 325K for T4 and 20K for ΔT. Validation has been done from the algorithm result of MODIS dataset over Indonesia and South Africa. The accuracy of MODIS fire detection by MOD14 algorithm is 73.2% and 91.7% on MODIS data over Sumatra-Borneo and South Africa respectively

Analysing Threshold Value in Fire Detection Algorithm Using MODIS Data

MODIS instruments have been designed to include special channels for fire monitoring by adding more spectral thermal band detector on them. The basic understanding of remote sensing fire detection should be kept in mind to be able to improve the algorithm for regional scale detection purposes. It still gives many chances for more exploration. This paper describes the principle of fire investigation applied on MODIS data. The main used algorithm in this research is contextual algorithm which has been developed by NASA scientist team. By varying applied threshold of T4 value in the range of 320-360K it shows that detected fire is significantly changed. While significant difference of detected FHS by changing ΔT threshold value is occurred in the range of 15-35K. Improve and adjustment of fire detection algorithm is needed to get the best accuracy result proper to local or regional conditions. MOD14 algorithm is applied threshold values of 325K for T4 and 20K for ΔT. Validation has been done from the algorithm result of MODIS dataset over Indonesia and South Africa. The accuracy of MODIS fire detection by MOD14 algorithm is 73.2% and 91.7% on MODIS data over Sumatra-Borneo and South Africa respectively

Improving the optimization solution for a semi-analytical shallow water inversion model in the presence of spectrally correlated noise

In coastal regions, shallow water semi-analytical inversion algorithms may be used to derive geophysical parameters such as inherent optical properties (IOPs), water column depth, and bottom albedo coefficients by inverting sensor-derived sub-surface remote sensing reflectance, rrs. The uncertainties of these derived geophysical parameters due to instrumental and environmental noise can be estimated numerically via the addition of spectral noise to the sensor-derived rrs before inversion. Repeating this process multiple times allows the calculation of the standard error and average for each derived parameter. Apart from spectral non-uniqueness, the optimization algorithm employed in the inversion must converge onto a single minimum to obtain a true representation of the uncertainty for a given set of noise-perturbed rrs. Failure to do so inflates the uncertainty and affects the average retrieved value (accuracy). We show that the standard approach of seeding the optimization with an arbitrary, fixed initial guess, can lead to the convergence to multiple minima, each having substantially different centroids in multi-parameter solution space. We present the Update-Repeat Levenberg-Marquardt (UR-LM) and Latin Hypercube Sampling (LHS) routines that dynamically search the solution space for an optimal initial guess, that when applied to the optimization allows convergence to the best local minimum. We apply the UR-LM and LHS methods on HICO-derived and simulated rrs and demonstrate the improved computational efficiency, precision, and accuracy afforded from these methods compared with the standard approach. Conceptually, these methods are applicable to remote sensing based, shallow water or oceanic semi-analytical inversion algorithms requiring nonlinear least squares optimization

Long-term spatial variations in turbidity and temperature provide new insights into coral-algal states on extreme/marginal reefs

Globally, coral reefs are under threat, with many exhibiting degradation or a shift towards algal-dominated regimes following marine heat waves, and other disturbance events. Marginal coral reefs existing under naturally extreme conditions, such as turbid water reefs, may be more resilient than their clear water counterparts as well as offer some insight into how reefs could look in the future under climate change. Here, we surveyed 27 benthic habitats across an environmental stress gradient in the Exmouth Gulf region of north Western Australia immediately following a marine heatwave event. We used multidecadal remotely sensed turbidity (from an in-situ validated dataset) and temperature, to assess how these environmental drivers influence variability in benthic communities and coral morphology. Long-term turbidity and temperature variability were associated with macroalgal colonisation when exceeding a combined threshold. Coral cover was strongly negatively associated with temperature variability, and positively associated with depth, and wave power, while coral morphology diversity was positively associated with turbidity. While moderate turbidity (long-term average ~ 2 mg/L suspended matter) appeared to raise the threshold for coral bleaching and macroalgal dominance, regions with higher temperature variability (> 3.5 °C) appeared to have already reached this threshold. The region with the least turbidity and temperature variability had the highest amount of coral bleaching from a recent heatwave event and moderate levels of both these variables may confer resilience to coral reefs

Identifying Metocean Drivers of Turbidity Using 18 Years of MODIS Satellite Data: Implications for Marine Ecosystems under Climate Change

Turbidity impacts the growth and productivity of marine benthic habitats due to light limitation. Daily/monthly synoptic and tidal influences often drive turbidity fluctuations, however, our understanding of what drives turbidity across seasonal/interannual timescales is often limited, thus impeding our ability to forecast climate change impacts to ecologically significant habitats. Here, we analysed long term (18-year) MODIS-aqua data to derive turbidity and the associated meteorological and oceanographic (metocean) processes in an arid tropical embayment (Exmouth Gulf in Western Australia) within the eastern Indian Ocean. We found turbidity was associated with El Niño Southern Oscillation (ENSO) cycles as well as Indian Ocean Dipole (IOD) events. Winds from the adjacent terrestrial region were also associated with turbidity and an upward trend in turbidity was evident in the body of the gulf over the 18 years. Our results identify hydrological processes that could be affected by global climate cycles undergoing change and reveal opportunities for managers to reduce impacts to ecologically important ecosystems

Machine learning regression model for predicting honey harvests

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. Honey yield from apiary sites varies significantly between years. This affects the beekeeper’s ability to manage hive health, as well as honey production. This also has implications for ecosystem services, such as forage availability for nectarivores or seed sets. This study investigates whether machine learning methods can develop predictive harvest models of a key nectar source for honeybees, Corymbia calophylla (marri) trees from South West Australia, using data from weather stations and remotely sensed datasets. Honey harvest data, weather and vegetation-related datasets from satellite sensors were input features for machine learning algorithms. Regression trees were able to predict the marri honey harvested per hive to a Mean Average Error (MAE) of 10.3 kg. Reducing input features based on their relative model importance achieved a MAE of 11.7 kg using the November temperature as the sole input feature, two months before marri trees typically start to produce nectar. Combining weather and satellite data and machine learning has delivered a model that quantitatively predicts harvest potential per hive. This can be used by beekeepers to adaptively manage their apiary. This approach may be readily applied to other regions or forage species, or used for the assessment of some ecosystem services

A method to analyze the potential of optical remote sensing for benthic habitat mapping

Quantifying the number and type of benthic classes that are able to be spectrally identified in shallow water remote sensing is important in understanding its potential for habitat mapping. Factors that impact the effectiveness of shallow water habitat mapping include water column turbidity, depth, sensor and environmental noise, spectral resolution of the sensor and spectral variability of the benthic classes. In this paper, we present a simple hierarchical clustering method coupled with a shallow water forward model to generate water-column specific spectral libraries. This technique requires no prior decision on the number of classes to output: the resultant classes are optically separable above the spectral noise introduced by the sensor, image based radiometric corrections, the benthos’ natural spectral variability and the attenuating properties of a variable water column at depth. The modeling reveals the effect reducing the spectral resolution has on the number and type of classes that are optically distinct. We illustrate the potential of this clustering algorithm in an analysis of the conditions, including clustering accuracy, sensor spectral resolution and water column optical properties and depth that enabled the spectral distinction of the seagrass Amphibolis antartica from benthic algae

Intercomparison of shallow water bathymetry, hydro-optics, and benthos mapping techniques in Australian and Caribbean coastal environments

Science, resource management, and defense need algorithms capable of using airborne or satellite imagery to accurately map bathymetry, water quality, and substrate composition in optically shallow waters. Although a variety of inversion algorithms are available, there has been limited assessment of performance and no work has been published comparing their accuracy and efficiency. This paper compares the absolute and relative accuracies and computational efficiencies of one empirical and five radiative-transfer-based published approaches applied to coastal sites at Lee Stocking Island in the Bahamas and Moreton Bay in eastern Australia. These sites have published airborne hyperspectral data and field data. The assessment showed that (1) radiative-transfer-based methods were more accurate than the empirical approach for bathymetric retrieval, and the accuracies and processing times were inversely related to the complexity of the models used; (2) all inversion methods provided moderately accurate retrievals of bathymetry, water column inherent optical properties, and benthic reflectance in waters less than 13 m deep with homogeneous to heterogeneous benthic/substrate covers; (3) slightly higher accuracy retrievals were obtained from locally parameterized methods; and (4) no method compared here can be considered optimal for all situations. The results provide a guide to the conditions where each approach may be used (available image and field data and processing capability). A re-analysis of these same or additional sites with satellite hyperspectral data with lower spatial and radiometric resolution, but higher temporal resolution would be instructive to establish guidelines for repeatable regional to global scale shallow water mapping approaches

Bottom Reflectance in Ocean Color Satellite Remote Sensing for Coral Reef Environments

Most ocean color algorithms are designed for optically deep waters, where the seafloor has little or no effect on remote sensing reflectance. This can lead to inaccurate retrievals of inherent optical properties (IOPs) in optically shallow water environments. Here, we investigate in situ hyperspectral bottom reflectance signatures and their separability for coral reef waters, when observed at the spectral resolutions of MODIS and SeaWiFS sensors. We use radiative transfer modeling to calculate the effects of bottom reflectance on the remote sensing reflectance signal, and assess detectability and discrimination of common coral reef bottom classes by clustering modeled remote sensing reflectance signals. We assess 8280 scenarios, including four IOPs, 23 depths and 45 bottom classes at MODIS and SeaWiFS bands. Our results show: (i) no significant contamination (Rrscorr 17 m for MODIS and >19 m for SeaWiFS for the brightest spectral reflectance substrate (light sand) in clear reef waters; and (ii) bottom cover classes can be combined into two distinct groups, “light” and “dark”, based on the modeled surface reflectance signals. This study establishes that it is possible to efficiently improve parameterization of bottom reflectance and water-column IOP retrievals in shallow water ocean color models for coral reef environments