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

Toward Automated Machine Learning-Based Hyperspectral Image Analysis in Crop Yield and Biomass Estimation

The incorporation of autonomous computation and artificial intelligence (AI) technologies into smart agriculture concepts is becoming an expected scientific procedure. The airborne hyperspectral system with its vast area coverage, high spectral resolution, and varied narrow-band selection is an excellent tool for crop physiological characteristics and yield prediction. However, the extensive and redundant three-dimensional (3D) cube data processing and computation have made the popularization of this tool a challenging task. This research integrated two important open-sourced systems (R and Python) combined with automated hyperspectral narrowband vegetation index calculation and the state-of-the-art AI-based automated machine learning (AutoML) technology to estimate yield and biomass, based on three crop categories (spring wheat, pea and oat mixture, and spring barley with red clover) with multifunctional cultivation practices in northern Europe and Estonia. Our study showed the estimated capacity of the empirical AutoML regression model was significant. The best coefficient of determination (R2) and normalized root mean square error (NRMSE) for single variety planting wheat were 0.96 and 0.12 respectively; for mixed peas and oats, they were 0.76 and 0.18 in the booting to heading stage, while for mixed legumes and spring barley, they were 0.88 and 0.16 in the reproductive growth stages. In terms of straw mass estimation, R2 was 0.96, 0.83, and 0.86, and NRMSE was 0.12, 0.24, and 0.33 respectively. This research contributes to, and confirms, the use of the AutoML framework in hyperspectral image analysis to increase implementation flexibility and reduce learning costs under a variety of agricultural resource conditions. It delivers expert yield and straw mass valuation two months in advance before harvest time for decision-makers. This study also highlights that the hyperspectral system provides economic and environmental benefits and will play a critical role in the construction of sustainable and intelligent agriculture techniques in the upcoming years

Using low‐cost drones to monitor heterogeneous submerged seaweed habitats: A case study in the Azores

1. Remote sensing is a powerful monitoring tool for seaweeds, providing large‐scale insights into their ecosystem benefits and invasive impacts. Satellites and manned aircraft have been widely used for this purpose, but their spatial resolution is generally insufficient to map heterogeneous seaweed habitats. / 2. In this study, the potential of low‐cost and high‐resolution drone imagery to map heterogeneous seaweed habitats was assessed on Azorean coasts, where an invasive and commercial species, Asparagopsis armata, is present. A Phantom Pro 3 drone equipped with a visible‐light sensor was used to create photomosaics in three sites on São Miguel island, and ground‐truth data for various seaweed groups were collected with exploratory kayak sampling. Support‐vector machine, random forest, and artificial neural network algorithms were used to construct predictive models of seaweed coverage. / 3. Wind, clouds, and sun glint were the most significant factors affecting drone surveys and images. Exploratory sampling helped locate relatively homogeneous seaweed patches; however, the data were limited and spatially autocorrelated, contributing to overoptimistic model evaluation metrics. Moreover, the models struggled to distinguish seaweeds deeper than 3–4 m. / 4. In conclusion, using drones to monitor heterogeneous seaweed habitats is challenging, especially in oceanic islands where waters are deep and weather is unpredictable. However, this study highlights the potential use of photo‐interpretation to collect modelling data from drone imagery, instead of time‐consuming exploratory ground‐truth sampling. Future studies could assess drones to map seaweeds in less challenging conditions and use photo‐interpretation to improve collection of modelling data

Mapping macrophytic vegetation in shallow lakes using the Compact Airborne Spectrographic Imager (CASI)

1. The ecological status of shallow lakes is highly dependent on the abundance and composition of macrophytes. However, large-scale surveys are often confined to a small number of water bodies and undertaken only infrequently owing to logistical and financial constraints. 2. Data acquired by the Compact Airborne Spectrographic Imager-2 (CASI-2) was used to map the distribution of macrophytes in the Upper Thurne region of the Norfolk Broads, UK. Three different approaches to image classification were evaluated: (i) Euclidean minimum distance, (ii) Gaussian maximum likelihood, and (iii) support vector machines. 3. The results show macrophyte growth-habits (i.e. submerged, floating-leaved, partially-emergent, emergent) and submerged species could be mapped with a maximum overall classification accuracy of 78% and 87%, respectively. The Gaussian maximum likelihood algorithm and support vector machine returned the highest classification accuracies in each instance. 4. This study suggests that remote sensing is a potentially powerful tool for large-scale assessment of the cover and distribution of aquatic vegetation in clear water shallow lakes, particularly with respect to upscaling field survey data to a functionally relevant form, and supporting site-condition monitoring under the European Union Habitats (92/43/EEC) and Water Framework (2000/60/EC) directives