66 research outputs found
Large Area Land Cover Mapping Using Deep Neural Networks and Landsat Time-Series Observations
This dissertation focuses on analysis and implementation of deep learning methodologies in the field of remote sensing to enhance land cover classification accuracy, which has important applications in many areas of environmental planning and natural resources management.
The first manuscript conducted a land cover analysis on 26 Landsat scenes in the United States by considering six classifier variants. An extensive grid search was conducted to optimize classifier parameters using only the spectral components of each pixel. Results showed no gain in using deep networks by using only spectral components over conventional classifiers, possibly due to the small reference sample size and richness of features. The effect of changing training data size, class distribution, or scene heterogeneity were also studied and we found all of them having significant effect on classifier accuracy.
The second manuscript reviewed 103 research papers on the application of deep learning methodologies in remote sensing, with emphasis on per-pixel classification of mono-temporal data and utilizing spectral and spatial data dimensions. A meta-analysis quantified deep network architecture improvement over selected convolutional classifiers. The effect of network size, learning methodology, input data dimensionality and training data size were also studied, with deep models providing enhanced performance over conventional one using spectral and spatial data. The analysis found that input dataset was a major limitation and available datasets have already been utilized to their maximum capacity.
The third manuscript described the steps to build the full environment for dataset generation based on Landsat time-series data using spectral, spatial, and temporal information available for each pixel. A large dataset containing one sample block from each of 84 ecoregions in the conterminous United States (CONUS) was created and then processed by a hybrid convolutional+recurrent deep network, and the network structure was optimized with thousands of simulations. The developed model achieved an overall accuracy of 98% on the test dataset. Also, the model was evaluated for its overall and per-class performance under different conditions, including individual blocks, individual or combined Landsat sensors, and different sequence lengths. The analysis found that although the deep model performance per each block is superior to other candidates, the per block performance still varies considerably from block to block. This suggests extending the work by model fine-tuning for local areas. The analysis also found that including more time stamps or combining different Landsat sensor observations in the model input significantly enhances the model performance
A survey of multiple classifier systems as hybrid systems
A current focus of intense research in pattern classification is the combination of several classifier systems, which can be built following either the same or different models and/or datasets building approaches. These systems perform information fusion of classification decisions at different levels overcoming limitations of traditional approaches based on single classifiers. This paper presents an up-to-date survey on multiple classifier system (MCS) from the point of view of Hybrid Intelligent Systems. The article discusses major issues, such as diversity and decision fusion methods, providing a vision of the spectrum of applications that are currently being developed
Object-based mapping of temperate marine habitats from multi-resolution remote sensing data
PhD ThesisHabitat maps are needed to inform marine spatial planning but current methods of field
survey and data interpretation are time-consuming and subjective. Object-based image
analysis (OBIA) and remote sensing could deliver objective, cost-effective solutions informed
by ecological knowledge. OBIA enables development of automated workflows to segment
imagery, creating ecologically meaningful objects which are then classified based on spectral
or geometric properties, relationships to other objects and contextual data. Successfully
applied to terrestrial and tropical marine habitats for over a decade, turbidity and lack of
suitable remotely sensed data had limited OBIA’s use in temperate seas to date. This thesis
evaluates the potential of OBIA and remote sensing to inform designation, management and
monitoring of temperate Marine Protected Areas (MPAs) through four studies conducted in
English North Sea MPAs.
An initial study developed OBIA workflows to produce circalittoral habitat maps from
acoustic data using sequential threshold-based and nearest neighbour classifications. These
methods produced accurate substratum maps over large areas but could not reliably predict
distribution of species communities from purely physical data under largely homogeneous
environmental conditions.
OBIA methods were then tested in an intertidal MPA with fine-scale habitat heterogeneity
using high resolution imagery collected by unmanned aerial vehicle. Topographic models
were created from the imagery using photogrammetry. Validation of these models through
comparison with ground truth measurements showed high vertical accuracy and the ability
to detect decimetre-scale features.
The topographic and spectral layers were interpreted simultaneously using OBIA, producing
habitat maps at two thematic scales. Classifier comparison showed that Random Forests
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outperformed the nearest neighbour approach, while a knowledge-based rule set produced
accurate results but requires further research to improve reproducibility.
The final study applied OBIA methods to aerial and LiDAR time-series, demonstrating that
despite considerable variability in the data, pre- and post-classification change detection
methods had sufficient accuracy to monitor deviation from a background level of natural
environmental fluctuation.
This thesis demonstrates the potential of OBIA and remote sensing for large-scale rapid
assessment, detailed surveillance and change detection, providing insight to inform choice of
classifier, sampling protocol and thematic scale which should aid wider adoption of these
methods in temperate MPAs.Natural Environment Research Council and Natural
Englan
Applications of Machine Learning in Chemical and Biological Oceanography
Machine learning (ML) refers to computer algorithms that predict a meaningful
output or categorize complex systems based on a large amount of data. ML is
applied in various areas including natural science, engineering, space
exploration, and even gaming development. This review focuses on the use of
machine learning in the field of chemical and biological oceanography. In the
prediction of global fixed nitrogen levels, partial carbon dioxide pressure,
and other chemical properties, the application of ML is a promising tool.
Machine learning is also utilized in the field of biological oceanography to
detect planktonic forms from various images (i.e., microscopy, FlowCAM, and
video recorders), spectrometers, and other signal processing techniques.
Moreover, ML successfully classified the mammals using their acoustics,
detecting endangered mammalian and fish species in a specific environment. Most
importantly, using environmental data, the ML proved to be an effective method
for predicting hypoxic conditions and harmful algal bloom events, an essential
measurement in terms of environmental monitoring. Furthermore, machine learning
was used to construct a number of databases for various species that will be
useful to other researchers, and the creation of new algorithms will help the
marine research community better comprehend the chemistry and biology of the
ocean.Comment: 58 Pages, 5 Figure
Seagrass and Seaweed Detection Approaches Using Remote Sensing and Google Earth Engine: A comparative Analysis of Different Machine Learning Techniques
Seagrasses and seaweed habitats contribute to crucial ecological services globally, from
capturing carbon dioxide and supporting 20% of the world’s largest fisheries to sustaining
the small, but many coastal communities [1]. Across the globe, an alarming decline in
their wild distribution has been recorded, attributed to climate change and direct pollution
[2]. Current estimates of how much the loss is are uncertain and mapping and monitoring
efforts are costly, data-intensive, and lack scalability. Thus, freely available data and
software in remote sensing, coupled with Machine Learning (ML) are deemed important
means to leverage existing mapping of seagrasses and seaweed spatial distribution [3, 4].
This thesis explored a free and scalable workflow by comparing three different ML
techniques mainly on Overall Accuracy (OA) and Tau(e) in classifying seagrass, seaweed,
and water. These are supervised, unsupervised, and semi-supervised learning (SSL) which
used data from the satellite, Sentinel-2 Level-2A, applied to a novel area of study, from
Biddeford Pool to Small Point at the Coast of Maine, United States of America. Results
showed that the SSL achieved the highest OA of 76% and Tau(e) = 0.72 on the hard test,
in line with previous work. To the best of the author’s knowledge, this work contributes
to the field of science by being the first in its field to use the geospatial analysis package
’geemap’, along with the software Google Earth Engine, and SSL for classifying seagrass
and seaweeds. Through demonstration, this work shows the potential of free data in remote
sensing, leveraged by ML to aid community monitoring in the environmental management
of seagrass and seaweed. The results here can be considered as a starting point for further
exploring the SSL paired with freely available data and community monitoring to lower
costs, handle data scarcity, and scale up in the field of aquatic vegetation mapping and
monitoring.Seagrasses and seaweed habitats contribute to crucial ecological services globally, from
capturing carbon dioxide and supporting 20% of the world’s largest fisheries to sustaining
the small, but many coastal communities [1]. Across the globe, an alarming decline in
their wild distribution has been recorded, attributed to climate change and direct pollution
[2]. Current estimates of how much the loss is are uncertain and mapping and monitoring
efforts are costly, data-intensive, and lack scalability. Thus, freely available data and
software in remote sensing, coupled with Machine Learning (ML) are deemed important
means to leverage existing mapping of seagrasses and seaweed spatial distribution [3, 4].
This thesis explored a free and scalable workflow by comparing three different ML
techniques mainly on Overall Accuracy (OA) and Tau(e) in classifying seagrass, seaweed,
and water. These are supervised, unsupervised, and semi-supervised learning (SSL) which
used data from the satellite, Sentinel-2 Level-2A, applied to a novel area of study, from
Biddeford Pool to Small Point at the Coast of Maine, United States of America. Results
showed that the SSL achieved the highest OA of 76% and Tau(e) = 0.72 on the hard test,
in line with previous work. To the best of the author’s knowledge, this work contributes
to the field of science by being the first in its field to use the geospatial analysis package
’geemap’, along with the software Google Earth Engine, and SSL for classifying seagrass
and seaweeds. Through demonstration, this work shows the potential of free data in remote
sensing, leveraged by ML to aid community monitoring in the environmental management
of seagrass and seaweed. The results here can be considered as a starting point for further
exploring the SSL paired with freely available data and community monitoring to lower
costs, handle data scarcity, and scale up in the field of aquatic vegetation mapping and
monitoring
Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences
The aim of the Special Issue “Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences” was to present a selection of innovative studies using hyperspectral imaging (HSI) in different thematic fields. This intention reflects the technical developments in the last three decades, which have brought the capacity of HSI to provide spectrally, spatially and temporally detailed data, favoured by e.g., hyperspectral snapshot technologies, miniaturized hyperspectral sensors and hyperspectral microscopy imaging. The present book comprises a suite of papers in various fields of environmental sciences—geology/mineral exploration, digital soil mapping, mapping and characterization of vegetation, and sensing of water bodies (including under-ice and underwater applications). In addition, there are two rather methodically/technically-oriented contributions dealing with the optimized processing of UAV data and on the design and test of a multi-channel optical receiver for ground-based applications. All in all, this compilation documents that HSI is a multi-faceted research topic and will remain so in the future
Monitoring and prediction of pasture quality and productivity using planet scope satellite data for sustainable livestock production systems in Colombia
As the population increases, demand for food increases too, which has led to large-scale land conversion to improve livestock production in Colombia. Fulfilling these criteria of increasing demand in a sustainable way is a challenge and remote sensing data provides an accurate method to support this task. In this study, Planet Scope multispectral satellite datasets and coincident field measurements acquired over test fields in the study area (PatĂa) of September 2018 was used. Fresh and dry weight biomass was calculated and forage quality analyses, crude protein (CP), in vitro dry matter digestibility (IVDMD), Ash and standing biomass dry weight (DM) was carried out in the forage nutritional quality laboratory of International Centre for Tropical Agriculture (CIAT). Field data was related to the remote sensing data using the random forest regression algorithm. R was required for the statistical analysis, to figure out the model performance for IVDMD, CP, Ash and DM. This project also investigated the spatial distribution of livestock which is affected by quality and area of potential forage zones. The R2 values of the regression models were 0.74 for IVDMD, 0.69 for CP, 0.38 for Ash and 0.49 for DM using a predictor combination of vegetation indices, simple ratios and bands
Uncertainty quantification for probabilistic machine learning in earth observation using conformal prediction
Unreliable predictions can occur when using artificial intelligence (AI)
systems with negative consequences for downstream applications, particularly
when employed for decision-making. Conformal prediction provides a
model-agnostic framework for uncertainty quantification that can be applied to
any dataset, irrespective of its distribution, post hoc. In contrast to other
pixel-level uncertainty quantification methods, conformal prediction operates
without requiring access to the underlying model and training dataset,
concurrently offering statistically valid and informative prediction regions,
all while maintaining computational efficiency. In response to the increased
need to report uncertainty alongside point predictions, we bring attention to
the promise of conformal prediction within the domain of Earth Observation (EO)
applications. To accomplish this, we assess the current state of uncertainty
quantification in the EO domain and found that only 20% of the reviewed Google
Earth Engine (GEE) datasets incorporated a degree of uncertainty information,
with unreliable methods prevalent. Next, we introduce modules that seamlessly
integrate into existing GEE predictive modelling workflows and demonstrate the
application of these tools for datasets spanning local to global scales,
including the Dynamic World and Global Ecosystem Dynamics Investigation (GEDI)
datasets. These case studies encompass regression and classification tasks,
featuring both traditional and deep learning-based workflows. Subsequently, we
discuss the opportunities arising from the use of conformal prediction in EO.
We anticipate that the increased availability of easy-to-use implementations of
conformal predictors, such as those provided here, will drive wider adoption of
rigorous uncertainty quantification in EO, thereby enhancing the reliability of
uses such as operational monitoring and decision making
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